From b1be27c1b75e071219662efb14ce5550e3ec0c58 Mon Sep 17 00:00:00 2001 From: igerber Date: Tue, 18 Aug 2026 16:42:52 -0400 Subject: [PATCH] feat(aggregation): CS post-fit aggregate() replays the bootstrap on bootstrapped fits CallawaySantAnnaResults.aggregate('event_study'/'group') on a bootstrapped fit (n_bootstrap > 0) previously raised NotImplementedError; the recompute levels now REPLAY the fit-time multiplier bootstrap from a kit-retained BootstrapReplaySpec and publish percentile inference matching a fit-time aggregation to BLAS reassociation (assert_allclose ~1 ULP on se/CI/cband; the discrete percentile p-value compared at 2/n_bootstrap). M-020 notes amendment; retires the TODO bootstrap re-aggregation row (#726). Mechanism (wholesale reuse): - _run_multiplier_bootstrap gains a keyword-only _replay_bitgen_state and snapshots the RNG state at weight-stream construction (nothing consumes the rng earlier, so the state fully determines the stream WITHIN one weight backend); the state + generation-branch identity ride CSBootstrapResults as plain __post_init__ attributes (public dataclass shape unchanged) into the kit's BootstrapReplaySpec (by value: seed=None fits replay, pickles carry it, post-fit set_params/attribute mutation cannot alter it). - staggered_results gains _KitBootstrapAggregator, a value-bound host re-running the SAME engine with the injected state; the fit-time override blocks (percentile se/CI/p + t, group df_used clearing, sup-t cband rows) are extracted verbatim into shared staggered_bootstrap helpers so fit and replay cannot drift. - Backend guard: Rust and NumPy generators produce DIFFERENT draws from the same bit-generator state (absolute Xoshiro row-seeding vs direct PCG64 stream), so the spec stamps bootstrap_chunking's new effective_weight_backend() at capture - branch-aware: stratified/ single-PSU survey generation and census-FPC zero weights are provably backend-independent and stamp "portable" - and the replay fails closed on a mismatch (and on backend=None) naming both backends and the remedies. Legacy pickles without the spec fail closed with a refit message. - The combined-IF gate in _prepare_event_study_aggregation also accepts precomputed-only callers (the kit-backed replay threads df=None). Ripples: - DiagnosticReport's ES-gated checks now RUN on bootstrapped plain CS fits (parallel_trends via Bonferroni fallback, pretrends_power/ sensitivity via the diagonal-covariance fallback; replay warnings recorded and republished per section). - practitioner_next_steps advises the post-fit route on bootstrapped CS fits (new pins; the deprecated fit-time kwarg form is gone from the advice bundle). - Sibling estimators' (EfficientDiD/Imputation/TwoStage/Continuous) bootstrap gates and the SDDD engine's fit-time override copy are untouched (the SDDD twin unification is sequenced with M-014; deferral recorded in the M-020 notes). Tests: TestBootstrapReplay (fit-time-vs-post-fit parity for ES/group/ balance_e against the NATIVE stored surface; seed=None idempotence; set_params/mutation immunity; pickle round-trip; relay order independence; legacy and backend fail-closed pins; low-draws re-warn pin), TestBootstrapReplayDesigns (bare-cluster PSU expansion, stratified survey portability, FPC, RCS, unbalanced panel, single-PSU NaN surfaces), TestBootstrapReplayConsumers (pretrends diag fallback + honest_did diagonal warning), effective_weight_backend unit tests, DR derives-and-runs + warning-republication pins, practitioner pins. Docs: M-020 notes + code_refs (+staggered_bootstrap.py, +bootstrap_chunking.py), REGISTRY CS aggregate note + relay-level re-warn scoping + weight-backend note in the Survey-Aware Bootstrap section, REPORTING.md, llms.txt / llms-full.txt / llms-practitioner.txt (the four-estimator exception block SPLIT: CS replays, siblings still raise), troubleshooting.rst, migration-4.0.md warning block + CS row blurbs, v4-design.md families count, CHANGELOG Unreleased entry, tutorials 02 (cells 19-21) and 09 (eight cells) rewritten to the post-fit route and re-executed. TODO row deleted; the DDD container-port and EfficientDiD cross-references trued up. --- CHANGELOG.md | 30 ++ TODO.md | 5 +- diff_diff/_staggered_triple_diff_engine.py | 2 + diff_diff/aggregation.py | 31 +- diff_diff/bootstrap_chunking.py | 16 + diff_diff/diagnostic_report.py | 12 +- diff_diff/guides/llms-full.txt | 30 +- diff_diff/guides/llms-practitioner.txt | 27 +- diff_diff/guides/llms.txt | 2 +- diff_diff/practitioner.py | 39 +- diff_diff/staggered.py | 132 ++--- diff_diff/staggered_bootstrap.py | 174 +++++- diff_diff/staggered_results.py | 149 +++++- docs/methodology/REGISTRY.md | 15 +- docs/methodology/REPORTING.md | 15 +- docs/migration-4.0.md | 23 +- docs/troubleshooting.rst | 17 +- docs/tutorials/02_staggered_did.ipynb | 310 +++++------ docs/tutorials/09_real_world_examples.ipynb | 559 ++++++++------------ docs/v4-deprecations.yaml | 4 +- docs/v4-design.md | 3 +- tests/test_aggregate_contract.py | 370 ++++++++++++- tests/test_bootstrap_chunking.py | 36 ++ tests/test_diagnostic_report.py | 40 +- tests/test_practitioner.py | 30 ++ 25 files changed, 1355 insertions(+), 716 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8cc4bb165..e8b695417 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -30,6 +30,36 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 docstring/REGISTRY/tutorial wording was corrected accordingly, and the old `test_coarser_partition_more_conservative` (whose DGP made the ordering an exact equality) was replaced by an identity pin + a genuine unbalanced-divergence test. +- **Post-fit `aggregate('event_study')`/`aggregate('group')` now work on bootstrapped + CallawaySantAnna fits** ([M-020] notes amendment; retires the TODO bootstrap + re-aggregation row). The recompute levels REPLAY the fit-time multiplier bootstrap + from a fit-retained `BootstrapReplaySpec` (the RNG state captured at weight-stream + construction, plus the run parameters BY VALUE): percentile se/CI and the sup-t + simultaneous band match a fit-time `fit(aggregate=...)` aggregation to + floating-point reassociation (`assert_allclose`, ~1 ULP — never bit-identity; the + discrete percentile p-value is a count statistic compared at `2/n_bootstrap`), the + container publishes no analytical provenance (`vcov`/`df` cleared), and + `balance_e=` composes. Properties: `seed=None` fits replay (the state is captured + by value); pickled results replay; post-fit `set_params`/attribute mutation of the + estimator cannot alter the replay. Caveats: each replaying call regenerates the + full weight stream and re-runs the fused perturbation GEMM over the per-cell + and per-event-time influence columns — O(n_bootstrap x n_units x (n_gt + + n_event_times)) FLOPs per call, no memoization by the aggregate() + immutability design; the replay re-runs the fit-time warning sites, + so warnings like the low-`n_bootstrap` notice can re-fire (the relay levels + 'simple'/'total' stay silent as before); and the spec is stamped with the + weight-generation backend — an artifact unpickled under the OTHER backend + (`DIFF_DIFF_BACKEND` flip, missing Rust extension, another machine) fails closed + with a refit message rather than silently regenerating a different bootstrap + realization (stratified/single-PSU survey and census-FPC generation is + backend-independent and stays portable). Pre-replay legacy pickles fail closed + with a refit message. Ripples: `DiagnosticReport`'s ES-gated checks now RUN on + bootstrapped plain CS fits (parallel trends via the Bonferroni fallback, + pretrends-power/sensitivity via the diagonal-covariance fallback, replay warnings + republished per section), and `practitioner_next_steps` advises the post-fit + route on bootstrapped CS fits instead of the deprecated fit-time kwarg. The + sibling estimators' (EfficientDiD/ImputationDiD/TwoStageDiD/ContinuousDiD) + bootstrapped recompute gates are unchanged. ## [3.9.1] - 2026-08-17 diff --git a/TODO.md b/TODO.md index 93b76bcb1..69d9482b3 100644 --- a/TODO.md +++ b/TODO.md @@ -22,7 +22,7 @@ Related tracking surfaces: | Issue | Location | Origin | Effort | Priority | |-------|----------|--------|--------|----------| | Expose cell-mass overall ATT (Stata `Post_avg` convention; = CS-simple on balanced panels) as an aggregate extra on LWDiD results — the fit's `.att` is the paper's `tau_omega` (cohort-mean-then-treated-weight, eq. 7.18); the authors' large-N display uses cell-mass weighting instead, and both are legitimate estimands (see the REGISTRY LWDiD Aggregation note). Lands only after PR #588 merges | `diff_diff/lwdid_results.py` | #588 | Quick | Low | -| Post-fit `aggregate()` for the staggered DDD container: `StaggeredTripleDiffResults` carries no `AggregationMixin`, which is why the phase-3(b) merge had to carry fit-time `aggregate=`/`balance_e=` onto the surviving `TripleDifference` (rows M-140/M-141) as the ONE documented exception to the section-6 aggregate-postfit program. Porting the container onto the M-122 aggregation contract retires both rows; note the bootstrapped-fit recompute levels will need draw retention or a fail-closed relay, the same problem tracked for CS/EfficientDiD/ImputationDiD. Until it lands, the DDD docs deliberately keep teaching the fit-time kwarg (the canonical route there) | `diff_diff/staggered_triple_diff_results.py`, `diff_diff/aggregation.py`, `docs/api/triple_diff.rst`, `docs/tutorials/08_triple_diff.ipynb` | 3(b) | Heavy | Medium | +| Post-fit `aggregate()` for the staggered DDD container: `StaggeredTripleDiffResults` carries no `AggregationMixin`, which is why the phase-3(b) merge had to carry fit-time `aggregate=`/`balance_e=` onto the surviving `TripleDifference` (rows M-140/M-141) as the ONE documented exception to the section-6 aggregate-postfit program. Porting the container onto the M-122 aggregation contract retires both rows; note the bootstrapped-fit recompute levels will need replay or a fail-closed relay — solved for CS via the BootstrapReplaySpec state replay (the container port can adopt the same mechanism); EfficientDiD/ImputationDiD/TwoStageDiD/ContinuousDiD still track theirs. Until it lands, the DDD docs deliberately keep teaching the fit-time kwarg (the canonical route there) | `diff_diff/staggered_triple_diff_results.py`, `diff_diff/aggregation.py`, `docs/api/triple_diff.rst`, `docs/tutorials/08_triple_diff.ipynb` | 3(b) | Heavy | Medium | | Staggered-DDD power support: `simulate_power`/`simulate_mde`/`simulate_sample_size` now REJECT a staggered-configured `TripleDifference` (both registered DDD generators emit 2x2x2 data and fit with `(group, partition, post)`, so a staggered config would be simulated under the wrong design). Support needs a staggered DDD DGP profile plus fit-kwargs builder, and a decision on whether the mode is selected by profile or by the estimator's own config | `diff_diff/power.py` | 3(b) | Mid | Low | | Bootstrap-`seed` provenance on multiplier-bootstrap results containers: neither `StaggeredTripleDiffResults` nor `CallawaySantAnnaResults` carries the `seed` that generated its bootstrap SEs / p-values / sup-t bands, so a serialized result cannot report the random configuration behind its inference. NOT a 3(b) regression - `seed` reaches the engine and `get_params()` correctly (same seed reproduces the SE bit-exactly, a different seed moves it), the gap is results-object observability only, it predates the merge, and both containers inherit it from the shared `CallawaySantAnnaBootstrapMixin`. Add `seed` (and consider `n_bootstrap`/`bootstrap_weights`/`cband`) to BOTH containers plus `to_dict()`, with seeded and unseeded pins; sequence it with the M-014 container unification rather than schema-changing one container mid-merge. Precedent for exposing it: `ContinuousDiDResults`, `EfficientDiDResults`, `SyntheticDiDResults` already do | `diff_diff/staggered_triple_diff_results.py`, `diff_diff/staggered_results.py` | 3(b) | Quick | Low | | `ContinuousDiD.pscore_trim` still validates `0.0 <= x < 0.5`, i.e. it admits `0`, while `TripleDifference` tightened to `0 < x < 0.5` in phase 3(b) (row M-142) on the grounds that `trim=0` disables the `np.clip(pscore, trim, 1-trim)` overlap guard keeping the `1/(1-p)` weights finite. The same argument applies to ContinuousDiD; aligning it was out of scope for a DDD merge and is recorded in the REGISTRY staggered-mode Note rather than left as silent drift. `TripleDifference` additionally gained a TYPE guard in 3(b) (reject bool/non-real-scalar/non-finite BEFORE the range comparison) because a bare `0 < x < 0.5` raises an incidental `TypeError` on `None`/str/complex/list, an ambiguous-truth error on a multi-element array, and silently ACCEPTS a 1-element array as the parameter; `ContinuousDiD`'s `np.isfinite(self.pscore_trim) and ...` has the same hole. Aligning both is one change - promote the guard to a shared `utils.validate_pscore_trim(value, *, allow_zero)` alongside `validate_n_bootstrap` rather than copying it | `diff_diff/continuous_did.py`, `diff_diff/utils.py` | 3(b) | Quick | Low | @@ -33,7 +33,7 @@ Related tracking surfaces: | `EventStudyResults` inference-provenance fields: the container records no `vcov_type`/`cluster_name`/`n_clusters`/`df_convention`/Conley metadata, so a serialized surface cannot distinguish unit auto-clustering from explicit clustering, survey, Conley, or the one-way carve-out (3(a) R9 review). Adding them is a cross-producer M-092 schema amendment (six builders, to_dict/summary rendering, surface-suite pins) - follow the pre-cut amendment convention (optional fields appended last, ledger note same-diff) rather than bolting onto one producer | `diff_diff/results_base.py` | 3(a) R9 | Mid | Low | | Opt-in singleton-group pruning for TwoWayFixedEffects (static + event-study mode; reghdfe parity): singleton units/periods are currently RETAINED class-wide - the within-demeaned row is zero so points are unchanged, but N/G/residual-df count it and CR1/finite-sample SEs shift (~0.41019 -> 0.40962 measured; REGISTRY "Deviation from R" Note, R5 review) - reghdfe iteratively drops singletons by default while fixest retains them (diff-diff matches fixest); an opt-in knob needs iterative unit+period pruning with consistent cluster/survey/replicate/Conley array subsetting and a default-flip decision protocol (moves published SEs) | `diff_diff/twfe.py`, `diff_diff/estimators.py`, `diff_diff/utils.py` | 3(a) R5 | Mid | Low | | Cohort-timing validation input for the simultaneous-adoption event-study family (TWFE `event_study=True` + MultiPeriodDiD through 3.9): an optional `first_treat=`/`cohort=` column so simultaneous adoption becomes checkable under the contract-valid time-invariant `D_i` indicator - today the staggered-adoption advisory derives timing from within-unit 0->1 transitions, so it can only fire on off-contract time-varying `D_it` input, and with valid `D_i` adoption timing is not observable in the inputs at all (REGISTRY "staggered-adoption detection limit" Notes, both sections); design questions: validate-only vs steering error, and interplay with the M-011 removal | `diff_diff/twfe.py`, `diff_diff/estimators.py` | 3(a) R2 | Mid | Medium | -| EfficientDiD `aggregate()` recompute levels (event_study/group) on bootstrapped fits fail closed ('simple' relays since the M-027 per-level convergence); wiring `BootstrapReplaySpec` (or retaining the n_bootstrap x n_gt draw matrix materialized at fit) would enable exact post-fit replay of percentile inference | `diff_diff/efficient_did_results.py`, `diff_diff/aggregation.py` | 2(b) PR-3a | Mid | Low | +| EfficientDiD `aggregate()` recompute levels (event_study/group) on bootstrapped fits fail closed ('simple' relays since the M-027 per-level convergence); wiring `BootstrapReplaySpec` (the CS mechanism: fit-captured RNG state + backend stamp, replayed post-fit — allclose to fit-time, not bit-identical, with a cross-backend fail-closed gate) would enable post-fit replay of percentile inference | `diff_diff/efficient_did_results.py`, `diff_diff/aggregation.py` | 2(b) PR-3a | Mid | Low | | ImputationDiD/TwoStageDiD `aggregate()` recompute levels on bootstrapped fits fail closed ('simple' relays since the M-027 per-level convergence; M-021/M-022); ImputationDiD's per-target psi machinery makes seeded replay tractable (the panel-backed kit retains everything the psi precompute reads), TwoStageDiD's per-level GMM scores are function-locals and would need retention | `diff_diff/imputation_results.py`, `diff_diff/two_stage_results.py`, `diff_diff/aggregation.py` | 2(b) PR-3b | Mid | Low | | ContinuousDiD `aggregate('event_study')` on bootstrapped fits fails closed (M-025); a seeded post-fit bootstrap-ES replay is tractable - the multiplier draws are seeded (`np.random.default_rng(self.seed)`) - but needs the FULL per-cell `_bootstrap_info` (bread/ee_treated/Psi_eval/dPsi_*/beta_pred) the pruned kit deliberately drops, so shipping it means a kit-payload change with its own memory contract | `diff_diff/continuous_did_aggregation.py`, `diff_diff/continuous_did_results.py` | 2(b) PR-3c | Mid | Low | | EfficientDiD, ImputationDiD, ContinuousDiD and HeterogeneousAdoptionDiD are the outstanding M-092 event-study df-provenance holes: the container's per-row df is all-NaN even on survey fits where a finite `_survey_df` governed the p-values (the container-level scalar `df_survey` IS exposed - the hole is the PER-ROW column only; no event_study_df/df_inference field; pre-existing, NOT a regression of the M-023 PR - today's builder output is identical). The kits now retain the scalar (ImputationDiD's since 2(b) PR-3b, ContinuousDiD's since 2(b) PR-3c - same shape: scalar `df_survey` exposed, per-row column all-NaN, identical to each fit-time surface); threading it into the per-row channel is a contained follow-up | `diff_diff/efficient_did_results.py`, `diff_diff/imputation_results.py`, `diff_diff/continuous_did_results.py`, `diff_diff/results_base.py` | 2(b) PR-3a | Quick | Low | @@ -51,7 +51,6 @@ Related tracking surfaces: | `WooldridgeDiD` DROPS the observations of a cohort with no supported pre-period before `g - anticipation` ([M-123]) rather than identifying it. Excluding the rows is correct given `g-1` normalization -- leaving them in silently loads the cohort's effect onto the time FE -- but dropping a cohort a user supplied is a lossy last resort. **Route (b) is now SETTLED NEGATIVELY and is not the answer:** the paper's no-never-treated last-cohort normalization shipped (W2025 Sec 5.4, per-period comparison support), and it does NOT identify these cohorts -- `wooldridge-2025-review.md:477` is explicit that in the final period the last cohort's ATT is unidentified, and the implementation still excludes any cohort whose reference is `None`. **Route (a) remains open:** an explicit user-supplied reference period per cohort -- W2025 Section 6.1 says any pre-treatment period may serve and the pre-trend `t`-test is invariant to the choice, so a cohort with ANY supported pre-period is a candidate even when `g-1` is missing. If route (a) also fails to identify the cohort, convert this row into a REGISTRY Note recording exclusion as the deliberate final answer. | `diff_diff/wooldridge.py`, `docs/methodology/REGISTRY.md` | #724 | Heavy | Medium | | `WooldridgeDiD` REFUSES a panel whose units split into disconnected support groups within a cohort, rather than estimating what IS identified. The connectivity guard (REGISTRY *within-cohort support connectivity*) correctly detects that a closed component's cells are collinear with the unit FE — previously QR dropped one silently and the overall ATT averaged an incomplete set (issue #724's failure mode via unit support). **Refusing is the safe answer, not the complete one.** The connected component containing the reference is still fully identified, so the estimable resolution is either (a) estimate the connected component and report the disconnected units as excluded, with the estimand restated (a sub-population of units, so it needs a REGISTRY definition and interacts with the survey-domain row above), or (b) per-component references, if a component with its own pre-period can carry its own normalization — needs a methodology decision, since components then are not comparable on one baseline. Gate with the split-support fixture in `TestWithinCohortSupportConnectivity`. | `diff_diff/wooldridge.py` | #724-codex-R7 | Heavy | Medium | | `WooldridgeDiD` fully resolves the `SurveyDesign` TWICE on every supported survey fit. The pre-exclusion validation pass (added so invalid metadata cannot hide in rows that cohort exclusion deletes) calls `survey_design.resolve(sample)`, and each fitter then calls `_resolve_survey_for_wooldridge` -> `_resolve_survey_for_fit` on the same frame, repeating weight normalization, strata/PSU/FPC validation and design-array construction. Any fit that REACHES the second resolve has an unchanged sample (survey + unidentified-cohort exclusion raises first), so the first result is reusable: capture the `_resolve_survey_for_fit` 4-tuple early and thread it into the three fitters as an optional `pre_resolved`. **Caveat that makes this non-trivial:** `sample = sample.reset_index(drop=True)` runs BETWEEN the two calls, so the reused object must be verified index-independent (resolution extracts positional numpy arrays, but `_inject_cluster_as_psu` and the metadata recompute need checking), and the early call must stop suppressing warnings or the user loses the weight-normalization notice. Gate with a survey fit asserting one normalization warning and byte-identical SEs. | `diff_diff/wooldridge.py` | #724-codex-R10 | Mid | Low | -| Bootstrap re-aggregation for `CallawaySantAnnaResults.aggregate()` recompute levels — a bootstrapped fit's event_study/group currently RAISE ('simple' relays since the M-027 per-level convergence) rather than substituting analytical inference for percentile-bootstrap statistics. The value-bound `BootstrapReplaySpec` (bit-identical replay, picklable, immune to post-fit `set_params`) is already in-tree and spike-verified; wiring it needs the per-`(g,t)` / per-event-time draw retention plus `assert_allclose` parity tests against the fit-time bootstrap numbers (NOT bit-identity — the fused GEMM's column count differs post-fit, ~1 ULP reassociation). | `diff_diff/aggregation.py`, `diff_diff/staggered_results.py` | #726 | Mid | Medium | | Consolidate the inference-df precedence duplicated across `honest_did.py` (3 copies at ~L655/L836/L1004) onto the shared `resolve_inference_df()` helper added in `diff_diff/aggregation.py`. The copies are correct today; the risk is drift if the survey/replicate precedence changes in one place only. (Adjacent but distinct from PR C's `utils.resolve_tail_df`: that is the FIT-TIME `df_convention` fallback resolver, this is a post-fit results READER.) | `diff_diff/honest_did.py` | #726 | Quick | Low | | `ContinuousDiD` CGBS-2024 remaining extensions (earlier phases — `covariates=` reg/dr, `treatment_type="discrete"`, single-cohort `control_group="lowest_dose"` with estimand `ATT(d)−ATT(d_L)` — are already supported; see REGISTRY Note #7). Remaining (all deferred `NotImplementedError`, documented): `estimation_method="ipw"` on the dose curve (scalar-adjustment / degenerate); `covariates=` × `survey_design=` (weighted OR + weighted nuisance IF); multi-cohort **heterogeneous-support** discrete aggregation (support-aware: average each dose only over the cohorts that observe it); **multi-cohort `lowest_dose`** (within-cohort `d_L` reference + support-aware cross-cohort aggregation); and **`covariates=` × `lowest_dose`** (conditional-PT-relative-to-`d_L` estimand). Single-cohort / 2-period / shared-support multi-cohort are supported. | `continuous_did.py` | CGBS-2024 | Heavy | Low | | `WooldridgeDiD` does not apply the W2025 Sec 5.4 `D_{G_max} x X` covariate normalization, and three sibling covariate rank deficiencies are pre-existing. Measured with the period range pinned and only the never-treated units toggled: (1) time-invariant `exovar` is absorbed by the unit FE, 4 of 26 columns, IDENTICALLY with and without never-treated units; (2) `xgvar`'s cell x covariate block, 19 of 41, identical on both panels; (3) `xtvar` under `demean_covariates=False` does exhibit the `sum_g D_g x = x` dependency that the default demeaning removes; (4) the newly-reachable case -- time-VARYING data passed through `exovar`, which its own docstring reserves for time-invariant covariates -- where the paper's `dT_i` rule would give a deterministic `D_{G_max} x X` drop instead of QR's arbitrary pick (coefficients unaffected, `1.35e-14`; `rank_deficient_action="error"` raises). REGISTRY's narrowed Sec 5.4 note cross-references this row. **Trap for whoever takes it:** `xtvar` under the DEFAULT `demean_covariates=True` is FULL RANK -- the raw block carries demeaned values while `D_g x X` carries raw ones -- and forcing the drop there moves `overall_att` 1.11903 -> 1.46269. Pinned as-is by `TestComparisonSupportFiltering::test_cells_derived_groups_did_not_leak_into_the_design`. | `diff_diff/wooldridge.py` | #729-followup | Heavy | Medium | diff --git a/diff_diff/_staggered_triple_diff_engine.py b/diff_diff/_staggered_triple_diff_engine.py index 3fa1235c8..886990547 100644 --- a/diff_diff/_staggered_triple_diff_engine.py +++ b/diff_diff/_staggered_triple_diff_engine.py @@ -145,6 +145,8 @@ def _run_multiplier_bootstrap( unit: Optional[str] = None, precomputed: Any = None, cband: bool = True, + *, + _replay_bitgen_state: Optional[Dict[str, Any]] = None, ) -> Any: ... def _fit_staggered_core( diff --git a/diff_diff/aggregation.py b/diff_diff/aggregation.py index a26a53a37..a62bf89f1 100644 --- a/diff_diff/aggregation.py +++ b/diff_diff/aggregation.py @@ -553,7 +553,11 @@ class AggregationKit: ``cband_crit_value`` is ``None`` both when bands were disabled and when no aggregation ran, so it cannot distinguish the two. bootstrap : AggregationKit.BootstrapReplaySpec or None - Value-bound bootstrap replay description; ``None`` on analytical fits. + Value-bound bootstrap replay description. Populated on + CallawaySantAnna bootstrapped fits (the recompute levels replay the + fit-time multiplier bootstrap from it); ``None`` on analytical fits + and on pre-replay legacy artifacts (whose bootstrapped recompute + levels fail closed with a refit message). """ bookkeeping: Dict[str, Any] @@ -574,9 +578,27 @@ class BootstrapReplaySpec: ``set_params(n_bootstrap=...)`` silently changes - and can truncate - the replayed stream. - This records the generator state plus the parameters BY VALUE and rebuilds - the stream through a module-level factory, which replays bit-identically, - pickles, and is immune to later mutation of the estimator. + This records the generator state plus the parameters BY VALUE, which + pickles and is immune to later mutation of the estimator. Two usage + modes: + + - ``rebuild()`` reconstructs the plain unit-level stream via + ``iter_weight_blocks`` (it does NOT cover the survey/FPC/PSU-expansion + branches). + - CallawaySantAnna's post-fit replay is STATE-ONLY: it consumes + ``bitgen_state``/``n_bootstrap``/``weight_type``/``backend`` and lets + ``_run_multiplier_bootstrap`` re-derive the generation branch from the + kit bookkeeping - one branch-selection implementation, no drift. + + ``backend`` records the weight-generation backend identity at capture + (``"rust"``/``"numpy"`` per + :func:`diff_diff.bootstrap_chunking.effective_weight_backend`, or + ``"portable"`` for provably backend-independent generation branches). + The Rust and NumPy generators produce DIFFERENT draws from the same + bit-generator state, so a replay under a different backend must FAIL + CLOSED rather than silently regenerate another realization. ``None`` + means unknown and also fails closed - a permissive default on a safety + discriminator would let a future constructor silently bypass the guard. """ bitgen_state: Dict[str, Any] @@ -585,6 +607,7 @@ class BootstrapReplaySpec: weight_type: str block_size: Optional[int] = None expand_index: Optional[np.ndarray] = None + backend: Optional[str] = None def rebuild(self) -> Any: """Reconstruct the replayable weight stream.""" diff --git a/diff_diff/bootstrap_chunking.py b/diff_diff/bootstrap_chunking.py index d0cc0997b..edc7db490 100644 --- a/diff_diff/bootstrap_chunking.py +++ b/diff_diff/bootstrap_chunking.py @@ -70,6 +70,22 @@ _TARGET_BLOCK_BYTES = 256 * 1024 * 1024 +def effective_weight_backend() -> str: + """The weight-generation backend :func:`iter_weight_blocks` would use NOW. + + Returns ``"rust"`` exactly when the generator branch below does — the + predicate must stay identical to :func:`iter_weight_blocks`'s own + ``rust_gen`` resolution. The two backends produce DIFFERENT draws from + the same bit-generator state (Rust draws one base seed and row-seeds + Xoshiro absolutely; the NumPy fallback consumes the PCG64 stream + directly), so a captured RNG state replays bit-identically only within + one backend. Post-fit bootstrap replay (CallawaySantAnna's + ``BootstrapReplaySpec``) stamps this value at fit and fails closed on a + mismatch rather than silently regenerating a different realization. + """ + return "rust" if (HAS_RUST_BACKEND and _rust_bootstrap_weights is not None) else "numpy" + + def compute_block_size( n_units: int, n_bootstrap: int, target_bytes: int = _TARGET_BLOCK_BYTES ) -> int: diff --git a/diff_diff/diagnostic_report.py b/diff_diff/diagnostic_report.py index be3ae7674..b97a1b82c 100644 --- a/diff_diff/diagnostic_report.py +++ b/diff_diff/diagnostic_report.py @@ -16,7 +16,10 @@ recompute — for ImputationDiD a panel-backed recompute, for TwoStageDiD a fresh Stage-2 OLS + GMM sandwich over the retained frame; used only when the raw ``event_study_effects`` field is absent, and failing - closed to an explicit skip on bootstrapped / kit-less fits), or + closed to an explicit skip on kit-less/legacy-pickle fits, the sibling + estimators' bootstrap gates, and backend-mismatched CS bootstrap + replays — bootstrapped CS fits themselves derive successfully via the + percentile-bootstrap replay), or produced by an existing diff-diff utility. May call ``check_parallel_trends`` / ``BaconDecomposition`` / ``EfficientDiD.hausman_pretest`` when the caller supplies the panel + @@ -840,8 +843,11 @@ def _resolve_event_study_surface( surface = candidate surface_dict = _surface_to_event_study_dict(candidate) except Exception as exc: # noqa: BLE001 — fail-soft by design: - # expected failures are NotImplementedError (bootstrap - # gates, pretrends+replicate) and ValueError (missing kit), + # expected failures are NotImplementedError (the sibling + # estimators' bootstrap gates, pretrends+replicate, CS + # legacy-pickle/backend-mismatch replay refusals — a + # bootstrapped CS fit itself now derives via the replay) + # and ValueError (missing kit), # but the surface builder can raise bare TypeError and this # resolver runs on the applicable_checks path with no outer # guard; an escaped exception would hard-fail the report. diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index a8003a597..8f3c4eb01 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -243,9 +243,9 @@ cs.fit( ```python from diff_diff import CallawaySantAnna, plot_event_study -# NOTE: no n_bootstrap here - post-fit aggregate() is ANALYTICAL-ONLY and -# recompute levels raise NotImplementedError on a bootstrapped fit -# ('simple' relays the stored bootstrap inference - see below). +# NOTE: works on bootstrapped fits too - the recompute levels REPLAY the +# fit-time multiplier bootstrap from the kit-retained RNG state +# (percentile inference; 'simple' relays the stored inference - see below). cs = CallawaySantAnna(estimation_method="dr") results = cs.fit(data, outcome='outcome', unit='unit', time='period', first_treat='first_treat') @@ -260,10 +260,13 @@ Fit-time `aggregate=` / `balance_e=` are DEPRECATED since 3.9 (removed in 4.0, ledger rows M-020 / M-117) and emit a `FutureWarning`. The downstream consumers all accept the post-fit container directly - `plot_event_study`, `compute_honest_did` and `compute_pretrends_power` each take -`results.aggregate('event_study')` - so the only case still requiring the -fit-time path is **bootstrap inference**: CallawaySantAnna's `aggregate()` -fails closed on a bootstrapped fit's RECOMPUTE levels ('event_study'/'group') rather than substituting analytical inference for -percentile-bootstrap statistics. +`results.aggregate('event_study')` - bootstrapped fits included: the +RECOMPUTE levels ('event_study'/'group') REPLAY the fit-time multiplier +bootstrap from the kit-retained RNG state (percentile se/CI/cband matching a +fit-time aggregation to floating-point reassociation; the container carries +vcov=None). Only pre-replay legacy pickles and artifacts moved across the +Rust/NumPy weight backend fail closed with a refit message - never a silent +substitution of analytical inference for percentile statistics. ```python # Post-fit route (recommended): aggregate once, feed any consumer. @@ -1711,10 +1714,11 @@ only: repeated-cross-section-routed and declared-`survey_design` fits raise `NotImplementedError` with the reason, as do bare-`cluster=` fits whose cohort-mass weighting diverges from the complete-case count). `balance_e=` applies to `"event_study"` only. Raises on -`"calendar"` (CS has no calendar aggregator) and, on a bootstrapped fit, on -the recompute levels (`"event_study"`/`"group"`) rather than substituting -analytical inference for percentile-bootstrap statistics - `"simple"` and, -where supported, `"total"` relay +`"calendar"` (CS has no calendar aggregator). On a bootstrapped fit the +recompute levels (`"event_study"`/`"group"`) replay the fit-time multiplier +bootstrap (percentile inference; re-emits the fit-time bootstrap warnings; +fails closed only for pre-replay legacy pickles and cross-weight-backend +artifacts) - `"simple"` and, where supported, `"total"` relay the stored bootstrap inference with a NaN df column (the per-level rule). ### SunAbrahamResults @@ -2925,7 +2929,9 @@ BR and DR do no estimator fitting — every effect, SE, p-value, CI, and sensitivity bound is read from the fitted result, derived from the result's own post-fit `aggregate('event_study')` surface (a view or retained-kit recompute, used only when the raw `event_study_effects` -field is absent; bootstrapped / kit-less fits fail closed to an +field is absent; bootstrapped CS fits derive via the percentile-bootstrap +replay, while kit-less/legacy-pickle fits, backend-mismatched replays, and +the sibling estimators' bootstrap gates fail closed to an explicit skip), or produced by an existing diff-diff utility (may call `check_parallel_trends`, `BaconDecomposition.fit`, or `EfficientDiD.hausman_pretest` when the panel + column kwargs are diff --git a/diff_diff/guides/llms-practitioner.txt b/diff_diff/guides/llms-practitioner.txt index a35b69bf2..bd4127182 100644 --- a/diff_diff/guides/llms-practitioner.txt +++ b/diff_diff/guides/llms-practitioner.txt @@ -361,12 +361,14 @@ estimated pre-periods exist). - For CS: pass the post-fit container - `compute_honest_did( results.aggregate('event_study'))` - no refit needed (the fit-time - `aggregate=` route is deprecated since 3.9). EXCEPTION: on a - BOOTSTRAPPED fit (`n_bootstrap > 0`) the post-fit recompute levels - (`'event_study'`/`'group'`) raise, while `aggregate('simple')` and, - where supported, `aggregate('total')` - relay the stored bootstrap inference; use the retained fit-time - `aggregate='event_study'` for a bootstrapped event-study surface. + `aggregate=` route is deprecated since 3.9). BOOTSTRAPPED fits + (`n_bootstrap > 0`) included: the recompute levels REPLAY the + fit-time multiplier bootstrap from the kit-retained RNG state + (percentile inference; the container carries no joint covariance, so + HonestDiD uses its diagonal approximation), while + `aggregate('simple')`/`aggregate('total')` relay the stored + inference; only pre-replay legacy pickles and cross-weight-backend + artifacts fail closed with a refit message. - For dCDH: requires `L_max >= 1` (multi-horizon mode). Bounds use placebo estimates `DID^{pl}_l` as pre-period coefficients rather than standard event-study pre-treatment coefficients, and use diagonal variance (no @@ -446,12 +448,13 @@ print(results.aggregate('event_study', balance_e=2).to_dataframe()) # NEW in 3.10 - the estimator-owned TOTAL incremental outcome (exact # relay C x overall; single row; panel non-survey fits only): print(results.aggregate('total').to_dataframe()) -# EXCEPTION: on a BOOTSTRAPPED fit the RECOMPUTE levels -# (event_study/group) raise on CS/EfficientDiD/ImputationDiD/TwoStageDiD -# — aggregate('simple') and, where supported, aggregate('total') relay -# the stored bootstrap inference; use the -# deprecated fit-time aggregation for a bootstrapped ES/group surface: -results = cs.fit(data, ..., aggregate='all') +# BOOTSTRAPPED fits: CS's recompute levels (event_study/group) REPLAY +# the fit-time multiplier bootstrap post-fit (percentile inference; no +# refit needed) — but they still RAISE on EfficientDiD/ImputationDiD/ +# TwoStageDiD, where aggregate('simple') and, where supported, +# aggregate('total') relay the stored bootstrap inference and the +# deprecated fit-time aggregation remains the ES/group route: +results = edid.fit(data, ..., aggregate='all') # EfficientDiD et al. only ``` ### For ContinuousDiD (MIXED post-fit `aggregate()`, row M-025) diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt index 59f7321cd..8369d7a54 100644 --- a/diff_diff/guides/llms.txt +++ b/diff_diff/guides/llms.txt @@ -21,7 +21,7 @@ diagnostic steps produces unreliable results. 4. **Choose estimator** — staggered adoption → CS/SA/BJS (NOT plain TWFE); few treated units → SDiD; factor confounding → TROP; simple 2x2 → DiD. Run `BaconDecomposition` to diagnose TWFE bias. 5. **Estimate** — `estimator.fit(data, ...)`. Always print the cluster count first and choose inference method based on the result (cluster-robust if >= 50 clusters, wild bootstrap if fewer — for DifferenceInDifferences pass `cluster=`; TwoWayFixedEffects auto-clusters at unit level). 6. **Sensitivity analysis** — `compute_honest_did(results)` for bounds under PT violations (MultiPeriodDiD, CS, or dCDH natively; the TwoWayFixedEffects `event_study=True` surface and a StackedDiD `results.aggregate('event_study')` container also admit - Stacked needs `kappa_pre >= 2` so estimated pre-periods exist), `run_all_placebo_tests()` for 2x2 falsification, specification comparisons for staggered designs. -7. **Heterogeneity** — CS: `results.aggregate('group')`/`.aggregate('event_study')` post-fit, no refit (fit-time `aggregate=`/`balance_e=` are deprecated since 3.9, removed in 4.0; `compute_honest_did` / `compute_pretrends_power` / `plot_event_study` all accept the post-fit `results.aggregate('event_study')` container directly; EXCEPTION: on a BOOTSTRAPPED CS fit the recompute levels `'event_study'`/`'group'` raise while `.aggregate('simple')` and, where supported, `.aggregate('total')` relay the stored bootstrap inference (NaN df column); use the fit-time aggregation for a bootstrapped event-study surface). NEW in 3.10: `.aggregate('total')` on CS/EfficientDiD/ImputationDiD/TwoStageDiD - the estimator-owned total incremental outcome (exact relay C x overall over the finite-masked complete-case support; single target='total' row; bootstrap-safe RELAY; panel non-survey fits only - repeated-cross-section and declared survey_design fits raise NotImplementedError with the reason); dCDH: `results.aggregate('event_study')`/`.aggregate('simple')` post-fit views (bootstrap fits included — pure views); SA: `results.event_study_effects`/`to_dataframe(level='cohort')`; StackedDiD: `results.aggregate('event_study')`/`.aggregate('simple')` post-fit views (the surface is ALWAYS computed at fit since 3.9 - row M-024 - and the container admits into `compute_honest_did`/`compute_pretrends_power` with `kappa_pre >= 2`); EDiD: `results.aggregate('event_study')`/`.aggregate('group')`/`.aggregate('simple')` post-fit, RECOMPUTED from retained EIFs (3.9, row M-023; fit-time `aggregate=`/`balance_e=` deprecated; on bootstrapped EDiD fits the recompute levels raise while `.aggregate('simple')` relays the stored bootstrap inference - use the fit-time aggregation for a bootstrapped ES/group surface; EDiD containers are NOT admitted into honest/pretrends - no joint ES covariance); BJS/TwoStageDiD: `results.aggregate('event_study')`/`.aggregate('group')`/`.aggregate('simple')` post-fit on ImputationDiD and TwoStageDiD too (3.9, rows M-021/M-022; recomputed from panel-backed kits, `balance_e=` on `aggregate('event_study')`; on bootstrapped fits the recompute levels raise while `.aggregate('simple')` relays the stored bootstrap inference - use the deprecated fit-time aggregation for a bootstrapped ES/group surface; their containers are not admitted into honest/pretrends - Imputation by design, TwoStage deferred pending a normalization derivation); CGBS continuous: ContinuousDiD is a MIXED adopter (3.9, row M-025) - `results.aggregate('dose')` (ATT(d)+ACRT(d) rows) and `.aggregate('simple')` (att+acrt rows) are views over the always-computed curves and work on ANY fit incl. bootstrapped, while `.aggregate('event_study')` recomputes the binarized event study from a pruned per-cell IF kit and raises on bootstrapped fits (use the deprecated fit-time `aggregate='eventstudy'` there until 4.0; its container is not admitted into honest/pretrends - no joint ES covariance and no reference normalization); HAD: `results.aggregate('simple')` (overall two-period fits; the target column carries the WAS estimand label) / `.aggregate('event_study')` (multi-period fits) - pure views, work on any fit (3.9, rows M-027/M-139; fit() selects the mode from the panel shape; HAD containers are not admitted into honest/pretrends - no joint cross-horizon covariance, deferred); subgroup re-estimation. +7. **Heterogeneity** — CS: `results.aggregate('group')`/`.aggregate('event_study')` post-fit, no refit (fit-time `aggregate=`/`balance_e=` are deprecated since 3.9, removed in 4.0; `compute_honest_did` / `compute_pretrends_power` / `plot_event_study` all accept the post-fit `results.aggregate('event_study')` container directly; BOOTSTRAPPED CS fits included: the recompute levels `'event_study'`/`'group'` REPLAY the fit-time multiplier bootstrap from the kit's retained RNG state (percentile inference matching a fit-time aggregation; container carries vcov=None), while `.aggregate('simple')` and, where supported, `.aggregate('total')` relay the stored bootstrap inference (NaN df column); only pre-replay legacy pickles and cross-weight-backend artifacts fail closed with a refit message). NEW in 3.10: `.aggregate('total')` on CS/EfficientDiD/ImputationDiD/TwoStageDiD - the estimator-owned total incremental outcome (exact relay C x overall over the finite-masked complete-case support; single target='total' row; bootstrap-safe RELAY; panel non-survey fits only - repeated-cross-section and declared survey_design fits raise NotImplementedError with the reason); dCDH: `results.aggregate('event_study')`/`.aggregate('simple')` post-fit views (bootstrap fits included — pure views); SA: `results.event_study_effects`/`to_dataframe(level='cohort')`; StackedDiD: `results.aggregate('event_study')`/`.aggregate('simple')` post-fit views (the surface is ALWAYS computed at fit since 3.9 - row M-024 - and the container admits into `compute_honest_did`/`compute_pretrends_power` with `kappa_pre >= 2`); EDiD: `results.aggregate('event_study')`/`.aggregate('group')`/`.aggregate('simple')` post-fit, RECOMPUTED from retained EIFs (3.9, row M-023; fit-time `aggregate=`/`balance_e=` deprecated; on bootstrapped EDiD fits the recompute levels raise while `.aggregate('simple')` relays the stored bootstrap inference - use the fit-time aggregation for a bootstrapped ES/group surface; EDiD containers are NOT admitted into honest/pretrends - no joint ES covariance); BJS/TwoStageDiD: `results.aggregate('event_study')`/`.aggregate('group')`/`.aggregate('simple')` post-fit on ImputationDiD and TwoStageDiD too (3.9, rows M-021/M-022; recomputed from panel-backed kits, `balance_e=` on `aggregate('event_study')`; on bootstrapped fits the recompute levels raise while `.aggregate('simple')` relays the stored bootstrap inference - use the deprecated fit-time aggregation for a bootstrapped ES/group surface; their containers are not admitted into honest/pretrends - Imputation by design, TwoStage deferred pending a normalization derivation); CGBS continuous: ContinuousDiD is a MIXED adopter (3.9, row M-025) - `results.aggregate('dose')` (ATT(d)+ACRT(d) rows) and `.aggregate('simple')` (att+acrt rows) are views over the always-computed curves and work on ANY fit incl. bootstrapped, while `.aggregate('event_study')` recomputes the binarized event study from a pruned per-cell IF kit and raises on bootstrapped fits (use the deprecated fit-time `aggregate='eventstudy'` there until 4.0; its container is not admitted into honest/pretrends - no joint ES covariance and no reference normalization); HAD: `results.aggregate('simple')` (overall two-period fits; the target column carries the WAS estimand label) / `.aggregate('event_study')` (multi-period fits) - pure views, work on any fit (3.9, rows M-027/M-139; fit() selects the mode from the panel shape; HAD containers are not admitted into honest/pretrends - no joint cross-horizon covariance, deferred); subgroup re-estimation. 8. **Robustness** — compare 2-3 estimators (CS vs SA vs BJS), MUST report with and without covariates (shows whether conditioning drives identification), present pre-trends and sensitivity bounds. After estimation, call `practitioner_next_steps(results)` for context-aware diff --git a/diff_diff/practitioner.py b/diff_diff/practitioner.py index 36b12ad61..31e06f744 100644 --- a/diff_diff/practitioner.py +++ b/diff_diff/practitioner.py @@ -396,35 +396,36 @@ def _handle_multi_period(results: Any): def _handle_cs(results: Any): - # The post-fit RECOMPUTE levels raise on a bootstrapped fit - # (percentile statistics are not retained for re-aggregation; - # 'simple'/'total' relay the stored quintet and stay available), so the - # event-study guidance must route those fits through the retained - # fit-time aggregation instead of advice that cannot run. + # The post-fit RECOMPUTE levels work on bootstrapped fits too: they + # REPLAY the fit-time multiplier bootstrap from the kit-retained RNG + # state (percentile inference matching a fit-time aggregation), so the + # advice is the same post-fit route on both regimes - only the caveats + # differ. Static guidance with a refit remedy is this module's + # convention; a legacy pickle without the replay state gets the refit + # message from aggregate() itself. is_bootstrap = getattr(results, "bootstrap_results", None) is not None if is_bootstrap: sensitivity_why = ( "Bounds the treatment effect under plausible violations of " - "parallel trends. This fit is BOOTSTRAPPED, and the post-fit " - "event-study/group recompute levels raise on bootstrap fits " - "(aggregate('simple') and, where supported, aggregate('total') " - "still relay the stored inference) - " - "refit with the fit-time aggregation to populate the " - "event-study surface." + "parallel trends. This fit is BOOTSTRAPPED: " + "aggregate('event_study') replays the fit-time multiplier " + "bootstrap post-fit (percentile inference; the container " + "carries no joint covariance, so HonestDiD uses its diagonal " + "approximation). A result predating the replay state asks for " + "a refit." ) sensitivity_code = ( "from diff_diff import compute_honest_did\n" - "# Bootstrap fit: the post-fit ES recompute raises - use the\n" - "# fit-time aggregation for the event-study surface:\n" - "results = cs.fit(data, ..., aggregate='event_study')\n" - "honest = compute_honest_did(results, method='relative_magnitude', M=1.0)\n" + "# Bootstrapped fit: aggregate post-fit - the replay reproduces\n" + "# the fit-time percentile inference (no refit needed):\n" + "es = results.aggregate('event_study')\n" + "honest = compute_honest_did(es, method='relative_magnitude', M=1.0)\n" "print(honest.summary())" ) heterogeneity_code = ( - "# Bootstrap fit: aggregate at fit time:\n" - "results = cs.fit(data, ..., aggregate='all')\n" - "print(results.group_effects) # Per-cohort ATTs\n" - "print(results.event_study_effects) # Dynamic effects" + "# Bootstrapped fit: aggregate post-fit - no refit needed:\n" + "print(results.aggregate('group').to_dataframe()) # Per-cohort ATTs\n" + "print(results.aggregate('event_study').to_dataframe()) # Dynamic effects" ) else: sensitivity_why = ( diff --git a/diff_diff/staggered.py b/diff_diff/staggered.py index 9f21720af..c729e6faa 100644 --- a/diff_diff/staggered.py +++ b/diff_diff/staggered.py @@ -15,6 +15,7 @@ from diff_diff._base import BaseEstimator from diff_diff.aggregation import ( AggregationKit, + BootstrapReplaySpec, ) from diff_diff.linalg import ( _check_propensity_diagnostics, @@ -31,6 +32,9 @@ from diff_diff.staggered_bootstrap import ( CallawaySantAnnaBootstrapMixin, CSBootstrapResults, + apply_bootstrap_event_study_overrides, + apply_bootstrap_group_overrides, + apply_cband_conf_ints, ) # Import from split modules @@ -1875,14 +1879,13 @@ def fit( ``compute_honest_did``, ``compute_pretrends_power`` and ``plot_event_study`` all accept the post-fit container from ``results.aggregate('event_study')`` directly, so no consumer - requires the fit-time surface anymore - EXCEPT on bootstrapped - fits (``n_bootstrap > 0``), where the post-fit RECOMPUTE - levels (``'event_study'``/``'group'``) raise (percentile - inference cannot be reproduced from the retained analytical - state; ``aggregate('simple')`` and, where supported, - ``aggregate('total')`` relay the stored bootstrap - inference and stay available) and fit-time aggregation - remains the supported route for those levels. Otherwise it + requires the fit-time surface anymore. Bootstrapped fits + (``n_bootstrap > 0``) included: the post-fit RECOMPUTE levels + (``'event_study'``/``'group'``) REPLAY the fit-time multiplier + bootstrap from the kit-retained RNG state (percentile + inference matching a fit-time aggregation to floating-point + reassociation; ``aggregate('simple')``/``('total')`` still + relay the stored inference bit-exactly). This parameter remains only as the deprecated compatibility path through 3.9. balance_e : int, optional @@ -2849,74 +2852,23 @@ def fit( group_time_effects[gt]["p_value"] = bootstrap_results.group_time_p_values[gt] group_time_effects[gt]["t_stat"] = float(gt_t_stats[idx]) - # Update event study effects with bootstrap SEs (batched) - if ( - event_study_effects is not None - and bootstrap_results.event_study_ses is not None - and bootstrap_results.event_study_cis is not None - and bootstrap_results.event_study_p_values is not None - ): - es_keys = [e for e in event_study_effects if e in bootstrap_results.event_study_ses] - if es_keys: - es_effects_arr = np.array( - [float(event_study_effects[e]["effect"]) for e in es_keys] - ) - es_ses_arr = np.array( - [float(bootstrap_results.event_study_ses[e]) for e in es_keys] - ) - es_t_stats, _, _, _ = safe_inference_batch( - es_effects_arr, es_ses_arr, alpha=self.alpha - ) - for idx, e in enumerate(es_keys): - event_study_effects[e]["se"] = bootstrap_results.event_study_ses[e] - event_study_effects[e]["conf_int"] = bootstrap_results.event_study_cis[e] - event_study_effects[e]["p_value"] = bootstrap_results.event_study_p_values[ - e - ] - event_study_effects[e]["t_stat"] = float(es_t_stats[idx]) - - # Update group effects with bootstrap SEs (batched) - if ( - group_effects is not None - and bootstrap_results.group_effect_ses is not None - and bootstrap_results.group_effect_cis is not None - and bootstrap_results.group_effect_p_values is not None - ): - grp_keys = [g for g in group_effects if g in bootstrap_results.group_effect_ses] - if grp_keys: - grp_effects_arr = np.array( - [float(group_effects[g]["effect"]) for g in grp_keys] - ) - grp_ses_arr = np.array( - [float(bootstrap_results.group_effect_ses[g]) for g in grp_keys] - ) - grp_t_stats, _, _, _ = safe_inference_batch( - grp_effects_arr, grp_ses_arr, alpha=self.alpha - ) - for idx, g in enumerate(grp_keys): - group_effects[g]["se"] = bootstrap_results.group_effect_ses[g] - group_effects[g]["conf_int"] = bootstrap_results.group_effect_cis[g] - group_effects[g]["p_value"] = bootstrap_results.group_effect_p_values[g] - group_effects[g]["t_stat"] = float(grp_t_stats[idx]) - # Same clearing rule the ES df provenance follows below: - # these se/p/CI are now percentile-bootstrap values that - # never used the analytical df, so keeping df_used would - # claim a t-reference that governed nothing. - group_effects[g]["df_used"] = None + # Update event study effects with bootstrap SEs (batched). + # Shared helper (verbatim extraction) so the post-fit aggregate() + # replay applies exactly the same overrides. + apply_bootstrap_event_study_overrides( + event_study_effects, bootstrap_results, self.alpha + ) + + # Update group effects with bootstrap SEs (batched) — same shared + # helper, including the df_used clearing rule. + apply_bootstrap_group_overrides(group_effects, bootstrap_results, self.alpha) # Compute simultaneous confidence band CIs if cband is available cband_crit_value = None if bootstrap_results is not None: cband_crit_value = bootstrap_results.cband_crit_value - if cband_crit_value is not None and event_study_effects is not None: - for e, eff_data in event_study_effects.items(): - se_val = eff_data["se"] - if np.isfinite(se_val) and se_val > 0: - eff_data["cband_conf_int"] = ( - eff_data["effect"] - cband_crit_value * se_val, - eff_data["effect"] + cband_crit_value * se_val, - ) + apply_cband_conf_ints(event_study_effects, cband_crit_value) # Consolidated _safe_inv rank-guard warning (sibling of PR #9 # finding #17). Rank-deficient PS Hessian / OR bread matrices in the @@ -3049,6 +3001,7 @@ def fit( influence_func_info, group_time_effects, is_survey_fit=survey_metadata is not None, + bootstrap_results=bootstrap_results, ) self.is_fitted_ = True @@ -5112,7 +5065,11 @@ def print_summary(self) -> None: #: `precomputed` keys the aggregation machinery actually reads. Verified by #: enumerating every `precomputed[...]` / `.get(...)` access in -#: `staggered_aggregation.py`. Everything else - notably `outcome_matrix`, +#: `staggered_aggregation.py` AND, since the post-fit bootstrap replay landed, +#: in `staggered_bootstrap.py` (`_run_multiplier_bootstrap` reads `all_units`, +#: `unit_to_idx`, `canonical_size`, `survey_weights`, `resolved_survey_unit` +#: through the kit) - any future prune audit must cover BOTH consumers. +#: Everything else - notably `outcome_matrix`, #: `covariate_matrix`, `obs_outcome`, `obs_covariates` - is DATA and is #: deliberately not retained, so a results object never holds the source panel. #: `_agg_cache` is excluded too: it is derived memoization, rebuilt on demand. @@ -5139,6 +5096,7 @@ def _build_aggregation_kit( group_time_effects: Optional[Dict[Any, Any]], *, is_survey_fit: bool = False, + bootstrap_results: Optional["CSBootstrapResults"] = None, ) -> Optional["AggregationKit"]: """Distil the fit-time state post-fit re-aggregation needs. @@ -5186,19 +5144,33 @@ def _build_aggregation_kit( if bookkeeping.get("unit_to_idx") is not None: bookkeeping["unit_to_idx"] = {i: i for i in range(n_kit_units)} + # Bootstrap replay: on a bootstrapped fit, retain the multiplier + # bootstrap's RNG state BY VALUE (plus the run parameters and the + # generation-branch backend identity) so aggregate('event_study'/'group') + # can re-run the fit-time bootstrap post-fit — bit-identical weight + # stream, statistics matching to BLAS reassociation. Values come off the + # returned CSBootstrapResults, never live estimator attributes, so a + # post-fit set_params/attribute mutation cannot alter the replay. + bootstrap_spec = None + if ( + bootstrap_results is not None + and bootstrap_results._replay_bitgen_state is not None + and precomputed is not None + ): + n_gen_units = int(precomputed.get("canonical_size", len(precomputed["all_units"]))) + bootstrap_spec = BootstrapReplaySpec( + bitgen_state=bootstrap_results._replay_bitgen_state, + n_bootstrap=bootstrap_results.n_bootstrap, + n_units=n_gen_units, + weight_type=bootstrap_results.weight_type, + backend=bootstrap_results._replay_backend, + ) + return AggregationKit( bookkeeping=bookkeeping, influence=influence_func_info, alpha=estimator.alpha, anticipation=estimator.anticipation, cband=bool(estimator.cband), - # Bootstrap replay is not wired: a bootstrapped fit's percentile - # inference cannot be reproduced from analytical state, so the - # RECOMPUTE levels (event_study/group) fail closed on one rather than - # silently substituting analytical numbers ('simple' and, where - # supported, 'total' relay the stored bootstrap inference and stay - # available - the per-level policy converged with row M-027). - # BootstrapReplaySpec (diff_diff/aggregation.py) is the verified - # mechanism for the follow-up. - bootstrap=None, + bootstrap=bootstrap_spec, ) diff --git a/diff_diff/staggered_bootstrap.py b/diff_diff/staggered_bootstrap.py index 235476b89..7bfef5b15 100644 --- a/diff_diff/staggered_bootstrap.py +++ b/diff_diff/staggered_bootstrap.py @@ -15,6 +15,7 @@ from diff_diff.bootstrap_chunking import ( ReplayableWeightStream, compute_block_size, + effective_weight_backend, iter_survey_multiplier_weight_blocks, iter_weight_blocks, tiled_if_matmul, @@ -31,6 +32,7 @@ from diff_diff.bootstrap_utils import ( compute_percentile_ci as _compute_percentile_ci_func, ) +from diff_diff.utils import safe_inference_batch if TYPE_CHECKING: import pandas as pd @@ -112,6 +114,19 @@ class CSBootstrapResults: overall_att_es_ci: Optional[Tuple[float, float]] = None overall_att_es_p_value: Optional[float] = None + def __post_init__(self) -> None: + # Post-fit replay bookkeeping, attached as PLAIN attributes (never + # dataclass fields) so the exported class's __init__ signature, + # dataclasses.fields() and asdict() stay unchanged — the same + # private-carrier pattern the results objects use for retained + # state. `_replay_bitgen_state` is the RNG snapshot taken at + # weight-stream construction; `_replay_backend` is the + # generation-branch identity ("rust"/"numpy", or "portable" for + # provably backend-independent branches). Both are populated by + # _run_multiplier_bootstrap on every run and pickle via __dict__. + self._replay_bitgen_state: Optional[Dict[str, Any]] = None + self._replay_backend: Optional[str] = None + # ============================================================================= # Bootstrap Mixin Class @@ -175,10 +190,21 @@ def _run_multiplier_bootstrap( unit: Optional[str] = None, precomputed: Any = None, cband: bool = True, + *, + _replay_bitgen_state: Optional[Dict[str, Any]] = None, ) -> CSBootstrapResults: """ Run multiplier bootstrap for inference on all parameters. + ``_replay_bitgen_state`` (keyword-only, package-internal): a + bit-generator state captured by a previous run. When given, the RNG + is restored to it instead of seeding from ``self.seed``, so the + multiplier-weight stream replays bit-identically — the post-fit + ``aggregate()`` replay path. Valid only under the SAME weight + backend that captured it (see + :func:`diff_diff.bootstrap_chunking.effective_weight_backend`); + the caller enforces the backend guard. + This implements the multiplier bootstrap procedure from Callaway & Sant'Anna (2021). The key idea is to perturb the influence function contributions with random weights at the cluster (unit) level, then recompute aggregations. @@ -218,6 +244,11 @@ def _run_multiplier_bootstrap( ) rng = np.random.default_rng(self.seed) + if _replay_bitgen_state is not None: + # Post-fit replay: restore the exact state the fit-time run + # captured at weight-stream construction. Nothing below consumes + # the rng before that point, so the stream is bit-identical. + rng.bit_generator.state = _replay_bitgen_state # Use global unit set for correct pg = n_g / N_total scaling. # Without this, pg is overestimated in unbalanced panels where some @@ -463,6 +494,31 @@ def _make_weight_iter( ) -> Iterator[Tuple[int, np.ndarray]]: return iter_weight_blocks(self.n_bootstrap, n_units, self.bootstrap_weights, rng_) + # Snapshot the rng state HERE — the value that fully determines the + # weight stream (nothing above consumed the rng; the survey psu + # resolution deliberately avoids it). Retained on the returned + # container for the post-fit aggregate() replay. `_replay_backend` + # records the generation-branch identity: "portable" for branches + # whose draws are provably identical under either weight backend — + # the stratified / single-PSU survey generator draws through the + # NumPy generator unconditionally, and the unstratified census-FPC + # case (fpc[0] <= n_psu, mirroring iter_survey_multiplier_weight_ + # blocks' fpc_zero) replaces every block with zeros — else the + # current effective backend, because Rust and NumPy produce + # DIFFERENT draws from the same bit-generator state. + replay_bitgen_state = dict(rng.bit_generator.state) + _backend_independent = False + if _use_survey_bootstrap: + assert resolved_survey_unit is not None + _fpc = getattr(resolved_survey_unit, "fpc", None) + _n_psu = len(psu_ids) # bound above in the survey branch + _backend_independent = ( + resolved_survey_unit.strata is not None + or _n_psu < 2 + or (_fpc is not None and _n_psu / _fpc[0] >= 1.0) + ) + replay_backend = "portable" if _backend_independent else effective_weight_backend() + # Re-iterable stream: each column tile of the fused perturbation GEMM # below makes its own full pass over the bit-identical weight stream. weight_stream = ReplayableWeightStream(_make_weight_iter, rng) @@ -640,7 +696,13 @@ def _make_weight_iter( group_effect_cis = None group_effect_p_values = None - if bootstrap_group is not None and group_agg_info is not None: + # ``group_list`` can be EMPTY when no cohort has a post-treatment cell + # (e.g. every treated cohort's onset lies beyond the observed panel): + # np.column_stack([]) would raise "need at least one array to + # concatenate", so guard it exactly as the event-study block guards + # empty ``rel_periods`` above — the group stats stay None and the + # aggregation returns its supported zero-row result. + if bootstrap_group is not None and group_agg_info is not None and group_list: grp_effects = np.array([group_agg_info[g]["effect"] for g in group_list]) grp_boot_matrix = np.column_stack([bootstrap_group[g] for g in group_list]) grp_ses, grp_ci_lo, grp_ci_hi, grp_pv = _compute_effect_bootstrap_stats_batch_func( @@ -718,7 +780,7 @@ def _make_weight_iter( group_effect_p_values = {k: np.nan for k in group_effect_p_values} cband_crit_value = None - return CSBootstrapResults( + result = CSBootstrapResults( n_bootstrap=self.n_bootstrap, weight_type=self.bootstrap_weights, alpha=self.alpha, @@ -740,6 +802,9 @@ def _make_weight_iter( overall_att_es_ci=overall_att_es_ci, overall_att_es_p_value=overall_att_es_p_value, ) + result._replay_bitgen_state = replay_bitgen_state + result._replay_backend = replay_backend + return result def _prepare_event_study_aggregation( self, @@ -835,8 +900,13 @@ def _agg_weight(g: Any, t: Any) -> float: "effect": agg_effect, } - # Compute combined IF for this event time if args available - if influence_func_info is not None and df is not None and unit is not None: + # Compute combined IF for this event time if args available. + # `precomputed` alone suffices for the in-package fast path + # (kit-backed post-fit replay threads df=None/unit=None); the + # df/unit pair remains for direct callers without precomputed. + if influence_func_info is not None and ( + precomputed is not None or (df is not None and unit is not None) + ): gt_pairs_for_e = [gt_pairs[i] for i in indices] groups_for_gt = np.array([gt_pairs[i][0] for i in indices]) combined_if, _ = self._compute_combined_influence_function( @@ -939,3 +1009,99 @@ def _compute_effect_bootstrap_stats( return _compute_effect_bootstrap_stats_func( original_effect, boot_dist, alpha=self.alpha, context=context ) + + +# ============================================================================= +# Bootstrap override helpers (shared by fit and the post-fit replay) +# ============================================================================= +# Extracted verbatim from CallawaySantAnna.fit()'s inline blocks so the +# post-fit aggregate() replay applies EXACTLY the same percentile overrides +# the fit-time path applies — one implementation, no twin drift. (The +# deprecated StaggeredTripleDifference keeps its OWN copy of the group +# replacement loop; unifying it is sequenced with the M-014 container port.) +# Note on warning attribution: when these run under the post-fit replay the +# engine's fit-tuned stacklevels resolve into library frames rather than the +# user's aggregate() call — accepted as cosmetic (recorded decision). + + +def apply_bootstrap_event_study_overrides( + event_study_effects: Optional[Dict[int, Dict[str, Any]]], + bootstrap_results: CSBootstrapResults, + alpha: float, +) -> None: + """Overwrite per-event-time se/CI/p with percentile-bootstrap values. + + Mutates ``event_study_effects`` in place; t is recomputed from the + percentile SE via ``safe_inference_batch``. No-op when either side has + no event-study surface. + """ + if ( + event_study_effects is not None + and bootstrap_results.event_study_ses is not None + and bootstrap_results.event_study_cis is not None + and bootstrap_results.event_study_p_values is not None + ): + es_keys = [e for e in event_study_effects if e in bootstrap_results.event_study_ses] + if es_keys: + es_effects_arr = np.array([float(event_study_effects[e]["effect"]) for e in es_keys]) + es_ses_arr = np.array([float(bootstrap_results.event_study_ses[e]) for e in es_keys]) + es_t_stats, _, _, _ = safe_inference_batch(es_effects_arr, es_ses_arr, alpha=alpha) + for idx, e in enumerate(es_keys): + event_study_effects[e]["se"] = bootstrap_results.event_study_ses[e] + event_study_effects[e]["conf_int"] = bootstrap_results.event_study_cis[e] + event_study_effects[e]["p_value"] = bootstrap_results.event_study_p_values[e] + event_study_effects[e]["t_stat"] = float(es_t_stats[idx]) + + +def apply_bootstrap_group_overrides( + group_effects: Optional[Dict[Any, Dict[str, Any]]], + bootstrap_results: CSBootstrapResults, + alpha: float, +) -> None: + """Overwrite per-group se/CI/p with percentile-bootstrap values. + + Mutates ``group_effects`` in place and clears each row's ``df_used`` + (the percentile inference never used the analytical df, so keeping it + would claim a t-reference that governed nothing). No-op when either + side has no group surface. + """ + if ( + group_effects is not None + and bootstrap_results.group_effect_ses is not None + and bootstrap_results.group_effect_cis is not None + and bootstrap_results.group_effect_p_values is not None + ): + grp_keys = [g for g in group_effects if g in bootstrap_results.group_effect_ses] + if grp_keys: + grp_effects_arr = np.array([float(group_effects[g]["effect"]) for g in grp_keys]) + grp_ses_arr = np.array([float(bootstrap_results.group_effect_ses[g]) for g in grp_keys]) + grp_t_stats, _, _, _ = safe_inference_batch(grp_effects_arr, grp_ses_arr, alpha=alpha) + for idx, g in enumerate(grp_keys): + group_effects[g]["se"] = bootstrap_results.group_effect_ses[g] + group_effects[g]["conf_int"] = bootstrap_results.group_effect_cis[g] + group_effects[g]["p_value"] = bootstrap_results.group_effect_p_values[g] + group_effects[g]["t_stat"] = float(grp_t_stats[idx]) + # Same clearing rule the ES df provenance follows: these + # se/p/CI are now percentile-bootstrap values that never used + # the analytical df, so keeping df_used would claim a + # t-reference that governed nothing. + group_effects[g]["df_used"] = None + + +def apply_cband_conf_ints( + event_study_effects: Optional[Dict[int, Dict[str, Any]]], + cband_crit_value: Optional[float], +) -> None: + """Attach simultaneous-band CIs per event time from the sup-t critical value. + + Mutates ``event_study_effects`` in place; no-op when the critical value + or the surface is absent. + """ + if cband_crit_value is not None and event_study_effects is not None: + for _e, eff_data in event_study_effects.items(): + se_val = eff_data["se"] + if np.isfinite(se_val) and se_val > 0: + eff_data["cband_conf_int"] = ( + eff_data["effect"] - cband_crit_value * se_val, + eff_data["effect"] + cband_crit_value * se_val, + ) diff --git a/diff_diff/staggered_results.py b/diff_diff/staggered_results.py index 10c813772..d515e7f91 100644 --- a/diff_diff/staggered_results.py +++ b/diff_diff/staggered_results.py @@ -6,7 +6,7 @@ """ from dataclasses import dataclass, field, replace -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple import numpy as np import pandas as pd @@ -17,15 +17,20 @@ build_total_relay_row, resolve_inference_df, ) +from diff_diff.bootstrap_chunking import effective_weight_backend from diff_diff.results import _format_survey_block, _get_significance_stars from diff_diff.results_base import BaseResults, build_event_study_surface from diff_diff.staggered_aggregation import ( CallawaySantAnnaAggregationMixin, fixed_cohort_agg_weights, ) - -if TYPE_CHECKING: - from diff_diff.staggered_bootstrap import CSBootstrapResults +from diff_diff.staggered_bootstrap import ( + CallawaySantAnnaBootstrapMixin, + CSBootstrapResults, + apply_bootstrap_event_study_overrides, + apply_bootstrap_group_overrides, + apply_cband_conf_ints, +) class _KitAggregator(CallawaySantAnnaAggregationMixin): @@ -47,6 +52,33 @@ def __init__(self, alpha: float, anticipation: int) -> None: self.anticipation = anticipation +class _KitBootstrapAggregator(CallawaySantAnnaBootstrapMixin, CallawaySantAnnaAggregationMixin): + """Runs the fit-time multiplier bootstrap off a retained kit (replay). + + Value-bound host for the post-fit bootstrap replay: it carries BY VALUE + everything ``_run_multiplier_bootstrap`` reads from its estimator host + (base order matches ``CallawaySantAnna`` — bootstrap mixin first), so the + replay is immune to post-fit ``set_params``/attribute mutation of the + estimator. The RNG is injected via ``_replay_bitgen_state`` rather than + seeded from ``self.seed``. Warning attribution note: the engine's + fit-tuned stacklevels resolve into library frames on this deeper + ``aggregate()`` chain (no ``_warn_frame_offset`` is set) — accepted as + cosmetic; the warnings themselves are accurate statements about the + replayed bootstrap. + """ + + _BOOTSTRAP_LABEL = "CallawaySantAnna" + + def __init__( + self, alpha: float, anticipation: int, n_bootstrap: int, bootstrap_weights: str + ) -> None: + self.alpha = alpha + self.anticipation = anticipation + self.n_bootstrap = n_bootstrap + self.bootstrap_weights = bootstrap_weights + self.seed = None # unused — the replay injects the captured state + + @dataclass class GroupTimeEffect: """ @@ -321,32 +353,82 @@ def _aggregate_compute( # M-027): 'simple' and 'total' are bit-exact RELAYS of the stored # overall inference - faithful under any inference regime, bootstrap # included (every SE branch is homogeneous of degree 1 in the x C - # scaling) - so they dispatch BEFORE the bootstrap gate. Only the - # RECOMPUTE levels below fail closed on bootstrapped fits. + # scaling) - so they dispatch BEFORE the bootstrap machinery. The + # RECOMPUTE levels below REPLAY the fit-time multiplier bootstrap on + # bootstrapped fits (kit-retained RNG state; fail-closed only for + # legacy pickles without the state and cross-backend artifacts). if level == "simple": return self._aggregate_simple_result(kit) if level == "total": return self._aggregate_total_result(kit) - if self.bootstrap_results is not None: - # Fail closed rather than silently handing back analytical numbers: - # a bootstrapped fit's se/p/CI are percentile statistics, and - # reproducing them post-fit needs retained draws (BootstrapReplaySpec). - raise NotImplementedError( - f"aggregate({level!r}) is not yet available on a bootstrapped " - "fit (n_bootstrap > 0): its inference is percentile-bootstrap " - "based and cannot be reproduced from the analytical state " - "retained here. aggregate('simple') and, where supported, " - "aggregate('total') relay the stored " - "bootstrap inference and remain available; otherwise re-fit " - "with the aggregation you need, or use n_bootstrap=0 for " - "analytical inference." - ) # Shallow copy: shares every array (no data is duplicated) but gives the # aggregators a scratch dict to memoize `_agg_cache` into, so the kit # itself is never written to. Mirrors what StaggeredTripleDifference - # already does when it aggregates through a modified copy. + # already does when it aggregates through a modified copy. Shared by + # the bootstrap replay and the analytical aggregation below, exactly + # as fit shares one `precomputed` between them. precomputed = dict(kit.bookkeeping) + + # Bootstrap replay: re-run the fit-time multiplier bootstrap from the + # kit-retained RNG state so the recompute levels carry percentile + # inference matching a fit-time aggregation (to BLAS reassociation, + # ~1 ULP on se/CI; p-values are count statistics). NOTE the cost: each + # replaying call regenerates the full weight stream and re-runs the + # fused GEMM over the per-cell + per-event-time influence columns - + # O(n_bootstrap x n_units x (n_gt + n_event_times)) FLOPs per call + # (no memoization: + # aggregate() returns a new object and never mutates self, and caching + # draws would retain matrices the kit design deliberately avoids). + boot_replay: Optional[CSBootstrapResults] = None + if self.bootstrap_results is not None: + spec = getattr(kit, "bootstrap", None) + if spec is None or getattr(spec, "bitgen_state", None) is None: + raise NotImplementedError( + f"aggregate({level!r}) on this bootstrapped fit needs the " + "fit-time bootstrap replay state, which this result " + "predates - refit to enable the percentile-bootstrap " + "replay. aggregate('simple') and, where supported, " + "aggregate('total') relay the stored bootstrap inference " + "and remain available." + ) + current_backend = effective_weight_backend() + if spec.backend not in ("portable", current_backend): + # A different weight backend regenerates a DIFFERENT + # multiplier-weight matrix from the same bit-generator state + # (Rust row-seeds absolutely from one base seed; NumPy + # consumes the PCG64 stream), so replaying here would + # silently desynchronize from this artifact's stored + # 'simple'/'total' relay inference. "portable"-stamped + # artifacts (backend-independent generation branches) replay + # anywhere. + raise NotImplementedError( + f"aggregate({level!r}) cannot replay this bootstrapped " + f"fit here: its multiplier bootstrap was generated under " + f"the {spec.backend!r} weight backend, but the current " + f"backend is {current_backend!r}, and the two produce " + "different draws from the same RNG state. Re-fit under " + "the current backend, or restore the original one (the " + "DIFF_DIFF_BACKEND environment variable / the Rust " + "extension install)." + ) + host = _KitBootstrapAggregator( + kit.alpha, kit.anticipation, spec.n_bootstrap, spec.weight_type + ) + boot_replay = host._run_multiplier_bootstrap( + group_time_effects=self.group_time_effects, + influence_func_info=kit.influence, + aggregate=level, + balance_e=balance_e if level == "event_study" else None, + treatment_groups=self.groups, + time_periods=self.time_periods, + df=None, + unit=None, + precomputed=precomputed, + cband=kit.cband, + _replay_bitgen_state=spec.bitgen_state, + ) + agg = _KitAggregator(kit.alpha, kit.anticipation) if level == "group": @@ -358,6 +440,11 @@ def _aggregate_compute( df=None, unit=None, ) + if boot_replay is not None: + # Same shared override helper fit uses: percentile se/CI/p + + # recomputed t, df_used cleared (percentile inference never + # used the analytical df). + apply_bootstrap_group_overrides(effects, boot_replay, kit.alpha) return self._group_effects_to_aggregation(effects, kit) # event_study -> the unified EventStudyResults container (row M-092) @@ -371,19 +458,30 @@ def _aggregate_compute( None, precomputed, ) + if boot_replay is not None: + # Mirror fit's clearing rules exactly: percentile se/CI/p + t + # override, sup-t cband rows, vcov/vcov_index/df cleared (the + # percentile inference never used them - false provenance + # otherwise). + apply_bootstrap_event_study_overrides(es.effects, boot_replay, kit.alpha) + apply_cband_conf_ints(es.effects, boot_replay.cband_crit_value) # `build_event_study_surface` reads the surface off a RESULTS object. # Under the immutability contract we cannot populate `self`, so a # throwaway carrier holds the freshly computed values. carrier = replace( self, event_study_effects=es.effects, - event_study_vcov=es.vcov, - event_study_vcov_index=es.vcov_index, - event_study_df=es.df_used, + event_study_vcov=None if boot_replay is not None else es.vcov, + event_study_vcov_index=None if boot_replay is not None else es.vcov_index, + event_study_df=None if boot_replay is not None else es.df_used, + cband_crit_value=( + boot_replay.cband_crit_value if boot_replay is not None else self.cband_crit_value + ), # Surface-faithful common-reference provenance: the aggregation # recomputes it over the RETAINED cohorts (balance_e can drop # the cohort responsible for a second base; the fit-level # fit-wide tuple would over-restrict the balanced container). + # Applies to the bootstrap branch identically. reference_event_times=es.reference_event_times, ) return build_event_study_surface(carrier) @@ -514,7 +612,8 @@ def _cs_total_mass(self, kit: Any) -> float: overcount that keeps RC routings gated) and this raises rather than publishing an ambiguous mass. Empty post set or all-NaN effects return NaN WITHOUT re-emitting the fit-time UserWarnings (the - no-post-fit-re-warn convention; the NaN row is the signal). + RELAY-level no-re-warn convention - the NaN row is the signal; the + RECOMPUTE levels' bootstrap replay re-emits by design). """ bk = kit.bookkeeping # TRAP: never bk["agg_total_weight"] - that is the RC all-units WIF diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index f1713378e..55e5fec66 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -1052,9 +1052,9 @@ The multiplier bootstrap uses random weights w_i with E[w]=0 and Var(w)=1: - **Note:** Repeated cross-sections (`panel=False`, Phase 7b): supports surveys like BRFSS, ACS annual, and CPS monthly where units are not followed over time. Uses cross-sectional DRDID (Sant'Anna & Zhao 2020, Section 4): `reg` matches `DRDID::reg_did_rc` (Eq 2.2), `dr` matches `DRDID::drdid_rc` (locally efficient, Eq 3.3+3.4 with 4 OLS fits), `ipw` matches `DRDID::std_ipw_did_rc`. Per-observation influence functions instead of per-unit. All three estimation methods support covariates and survey weights. - **Note:** Panel and RCS influence functions use the library-wide `phi_i = psi_i / n` convention (SE = `sqrt(sum(phi^2))`, algebraically equivalent to R's `sd(psi)*sqrt(n-1)/n`). Leading IF terms are computed on psi scale and divided by n; PS nuisance corrections are computed on psi scale (`score @ solve(Hessian)`) with a single `/n` conversion to phi. - **Note:** Non-survey DR path also includes nuisance IF corrections (PS + OR), matching the survey path structure (Phase 7a). Previously used plug-in IF only. As of v3.7 the non-survey reg and ipw paths carry their corrections too (OR estimation-effect / PS score), so the nuisance-IF treatment is method-uniform. -- **Note (post-fit aggregate() - rows M-020/M-117):** `fit(aggregate=)` is deprecated (3.9; removed 4.0) in favor of post-fit `results.aggregate(type, balance_e=)` on the fit-retained aggregation kit ('simple' relays the stored overall inference; 'event_study'/'group' recompute from the kit and fail closed on bootstrapped fits per the M-027 per-level rule). `DiagnosticReport` now derives the event-study container internally on plain fits (raw `event_study_effects` absent), so its `parallel_trends`, `pretrends_power`, and `sensitivity` checks run without the deprecated fit-time kwarg — CS containers are M-093-admitted into `compute_pretrends_power` / `HonestDiD.sensitivity_analysis` with pinned raw-route parity, and the raw field, when present (the requested-but-empty `{}` included), always takes precedence. `heterogeneity` is unaffected (it reads `group_time_effects` on plain fits). Bootstrapped fits surface the fail-closed `NotImplementedError` as an explicit per-check skip reason. +- **Note (post-fit aggregate() - rows M-020/M-117):** `fit(aggregate=)` is deprecated (3.9; removed 4.0) in favor of post-fit `results.aggregate(type, balance_e=)` on the fit-retained aggregation kit ('simple' relays the stored overall inference; 'event_study'/'group' recompute from the kit — on BOOTSTRAPPED fits they REPLAY the fit-time multiplier bootstrap from the kit's `BootstrapReplaySpec` RNG-state snapshot: percentile se/CI/cband matching a fit-time aggregation to BLAS reassociation (~1 ULP, `assert_allclose`, never bit-identity; the discrete percentile p-value is a count statistic outside that claim), vcov/df cleared (no analytical provenance beside percentile inference), backend-stamped and failing closed on a Rust-vs-NumPy weight-backend mismatch or a pre-replay legacy pickle — the M-027 per-level policy, with the former blanket fail-closed retired). `DiagnosticReport` now derives the event-study container internally on plain fits (raw `event_study_effects` absent), so its `parallel_trends`, `pretrends_power`, and `sensitivity` checks run without the deprecated fit-time kwarg — CS containers are M-093-admitted into `compute_pretrends_power` / `HonestDiD.sensitivity_analysis` with pinned raw-route parity, and the raw field, when present (the requested-but-empty `{}` included), always takes precedence. `heterogeneity` is unaffected (it reads `group_time_effects` on plain fits). Bootstrapped fits now DERIVE successfully via the percentile-bootstrap replay: the derived container carries `vcov=None`, so `parallel_trends` runs on the Bonferroni per-period fallback and `pretrends_power`/`sensitivity` ride the diagonal-covariance fallback; the replay's re-emitted fit-time warnings are recorded and republished per section. The fail-closed skip remains for kit-less/legacy pickles, backend-mismatched replays, and the sibling estimators' bootstrap gates. The recompute levels' replay RE-EMITS the fit-time bootstrap warnings by design (the relay levels' no-re-warn convention is scoped to 'simple'/'total'). -- **Note (post-fit `aggregate('total')` - the estimator-owned total incremental outcome, 3.10):** (a) **Estimand:** `total = C x overall_att` with `se = C x overall_se`, CI scaled by `C`, `t`/`p`/`df` inherited unchanged - an exact relay CONDITIONAL ON THE REALIZED AGGREGATION MASS (never call it unqualified "exact"): `C` is the design-fixed complete-case treated-observation count on every admitted routing, the inherited SE prices the overall's full inference (WIF/estimated-share terms included) but treats `C` as fixed at its realized value, and the random-mass `att*dC` variance term is deliberately out of scope (the DEFERRED survey/RC remainder). Every SE branch is homogeneous of degree 1 in the scaling, so the relay is exact under analytical AND percentile-bootstrap inference. (b) **Fail-closed routing gates**, in order: (1) repeated-cross-section-routed fits (`panel=False`, or genuinely-unbalanced `allow_unbalanced_panel=True` fits - a balanced panel with the flag stays panel-routed and admitted) raise `NotImplementedError` - the RC cohort mass counts UNITS and weights every kept post period by it, a ~T-fold overcount of treated observations on a T-wave RCS (execution-verified); (2) fits declaring a `survey_design=` raise - the realized-mass relay omits the survey mass-uncertainty term and design-aware population-scale totals are not implemented (retained weight scale differs by design family: analytic pweight resolves normalized to sum = n and CS retains no raw record - CS accepts pweight only - while replicate designs retain raw scale); the gate reads the fit-time `is_survey_fit` KIT snapshot, so bare-`cluster=` fits are ADMITTED (their synthesized all-ones internal design is not a survey) and a post-fit edit of the public `survey_metadata` cannot bypass it; the DECLARATION BOUNDARY is deliberate - an explicit unweighted `SurveyDesign(psu=...)` fit is numerically identical to bare `cluster=` yet fails closed by declaration (declaring survey_design opts into the survey contract; conservative, never wrong numbers; the message points at the `cluster=` route); a combined survey+RC fit deterministically gets the RC message; (3) the coincidence check: on bare-`cluster=` fits `_aggregate_simple` weights kept cells by the synthesized cohort masses (full cohort size per kept cell), and when kept cells have INCOMPLETE treated support that realized mass diverges from the complete-case count - the same overcount class as (1) - so the fit raises rather than publishing an ambiguous mass; clean bare-`cluster=` fits (the normal case) are admitted with `C` equal to both counts. Panel-routed pre-upgrade (legacy) kits missing the fit-time snapshots raise the refit message - never a mutable-field or public-replay fallback. (c) **Filter-replay contract:** `C` replays `_aggregate_simple`'s cell selection verbatim over the immutable `agg_gt_cells` fit-time kit snapshot (EDiD snapshot discipline; the kit builder stashes `(g, t, effect, n_treated)` 4-tuples in dict order) - anticipation filter FIRST (cells with `g - anticipation <= t < g` COUNT as post, the business-facing subtlety; universal-base reference cells are excluded by this filter alone, their base period being `< g - anticipation` by construction), finite-effect mask SECOND, weight = cohort mass where `fixed_cohort_agg_weights` provides one else per-cell complete-case `n_treated` (the source's `agg_weight` fallback is deliberately absent - RC-only, gated out), pairwise `np.sum`; the overall scalars come from the same stored fields the `'simple'` relay reads, so a post-fit mutation of `group_time_effects` moves NEITHER factor and `total == C x aggregate('simple').att` cannot desynchronize (pinned by a mutation test). NEVER use `agg_total_weight` (the RC all-units WIF mass). (d) **NaN rules:** empty post set / all-NaN effects give `C = NaN` and an all-NaN row WITHOUT re-emitting the fit-time UserWarnings (the no-post-fit-re-warn convention; the NaN row is the signal); with finite `C` the relay is VERBATIM - inherited NaNs (a degenerate fit's `(nan, nan)` CI beside finite att/se = 0) pass through, mirroring `aggregate('simple')` per the repo's non-estimable-row convention (att/n are never blanked by NaN inference fields); the ONLY additional blanking is true float overflow (a finite overall value made non-finite BY the `x C` scaling - unreachable in practice, guarded per this code family's finiteness convention). (e) **Bootstrap:** 'total' is a RELAY level - available on bootstrapped fits with the df column NaN (the M-027 per-level policy). (f) **Container conventions:** single row, `level="total"`, `label=["total"]`, `target=["total"]` (a total incremental outcome, NOT an ATT - the single-non-att neutral "estimate" rendering applies), `n = [C]` with `n_kind="obs"`, `weight=[1.0]`, per-class df carrier (CS: `resolve_inference_df` - finite on admitted bare-`cluster=` fits, NaN on plain analytical panel fits). Per-level `n` semantics are deliberately DIFFERENT: 'total''s `n` is the treated observations entering the support, while 'simple''s `n` stays treated+control units by contract - `n_kind` is per-container and each is documented, satisfying the never-conflate rule. (g) **MMM admission:** both `diff_diff.mmm` exporters accept the total container ALONE - `scale` (numeric or `"auto"`) is rejected as double-counting; for overall-total exports this route supersedes `scale="auto"` (see the MMM section). The total remains meaningful only for outcomes additive in levels - the same unverifiable caveat every scale route carries. +- **Note (post-fit `aggregate('total')` - the estimator-owned total incremental outcome, 3.10):** (a) **Estimand:** `total = C x overall_att` with `se = C x overall_se`, CI scaled by `C`, `t`/`p`/`df` inherited unchanged - an exact relay CONDITIONAL ON THE REALIZED AGGREGATION MASS (never call it unqualified "exact"): `C` is the design-fixed complete-case treated-observation count on every admitted routing, the inherited SE prices the overall's full inference (WIF/estimated-share terms included) but treats `C` as fixed at its realized value, and the random-mass `att*dC` variance term is deliberately out of scope (the DEFERRED survey/RC remainder). Every SE branch is homogeneous of degree 1 in the scaling, so the relay is exact under analytical AND percentile-bootstrap inference. (b) **Fail-closed routing gates**, in order: (1) repeated-cross-section-routed fits (`panel=False`, or genuinely-unbalanced `allow_unbalanced_panel=True` fits - a balanced panel with the flag stays panel-routed and admitted) raise `NotImplementedError` - the RC cohort mass counts UNITS and weights every kept post period by it, a ~T-fold overcount of treated observations on a T-wave RCS (execution-verified); (2) fits declaring a `survey_design=` raise - the realized-mass relay omits the survey mass-uncertainty term and design-aware population-scale totals are not implemented (retained weight scale differs by design family: analytic pweight resolves normalized to sum = n and CS retains no raw record - CS accepts pweight only - while replicate designs retain raw scale); the gate reads the fit-time `is_survey_fit` KIT snapshot, so bare-`cluster=` fits are ADMITTED (their synthesized all-ones internal design is not a survey) and a post-fit edit of the public `survey_metadata` cannot bypass it; the DECLARATION BOUNDARY is deliberate - an explicit unweighted `SurveyDesign(psu=...)` fit is numerically identical to bare `cluster=` yet fails closed by declaration (declaring survey_design opts into the survey contract; conservative, never wrong numbers; the message points at the `cluster=` route); a combined survey+RC fit deterministically gets the RC message; (3) the coincidence check: on bare-`cluster=` fits `_aggregate_simple` weights kept cells by the synthesized cohort masses (full cohort size per kept cell), and when kept cells have INCOMPLETE treated support that realized mass diverges from the complete-case count - the same overcount class as (1) - so the fit raises rather than publishing an ambiguous mass; clean bare-`cluster=` fits (the normal case) are admitted with `C` equal to both counts. Panel-routed pre-upgrade (legacy) kits missing the fit-time snapshots raise the refit message - never a mutable-field or public-replay fallback. (c) **Filter-replay contract:** `C` replays `_aggregate_simple`'s cell selection verbatim over the immutable `agg_gt_cells` fit-time kit snapshot (EDiD snapshot discipline; the kit builder stashes `(g, t, effect, n_treated)` 4-tuples in dict order) - anticipation filter FIRST (cells with `g - anticipation <= t < g` COUNT as post, the business-facing subtlety; universal-base reference cells are excluded by this filter alone, their base period being `< g - anticipation` by construction), finite-effect mask SECOND, weight = cohort mass where `fixed_cohort_agg_weights` provides one else per-cell complete-case `n_treated` (the source's `agg_weight` fallback is deliberately absent - RC-only, gated out), pairwise `np.sum`; the overall scalars come from the same stored fields the `'simple'` relay reads, so a post-fit mutation of `group_time_effects` moves NEITHER factor and `total == C x aggregate('simple').att` cannot desynchronize (pinned by a mutation test). NEVER use `agg_total_weight` (the RC all-units WIF mass). (d) **NaN rules:** empty post set / all-NaN effects give `C = NaN` and an all-NaN row WITHOUT re-emitting the fit-time UserWarnings (the RELAY-level no-re-warn convention — the NaN row is the signal; the recompute levels' bootstrap replay re-emits by design); with finite `C` the relay is VERBATIM - inherited NaNs (a degenerate fit's `(nan, nan)` CI beside finite att/se = 0) pass through, mirroring `aggregate('simple')` per the repo's non-estimable-row convention (att/n are never blanked by NaN inference fields); the ONLY additional blanking is true float overflow (a finite overall value made non-finite BY the `x C` scaling - unreachable in practice, guarded per this code family's finiteness convention). (e) **Bootstrap:** 'total' is a RELAY level - available on bootstrapped fits with the df column NaN (the M-027 per-level policy). (f) **Container conventions:** single row, `level="total"`, `label=["total"]`, `target=["total"]` (a total incremental outcome, NOT an ATT - the single-non-att neutral "estimate" rendering applies), `n = [C]` with `n_kind="obs"`, `weight=[1.0]`, per-class df carrier (CS: `resolve_inference_df` - finite on admitted bare-`cluster=` fits, NaN on plain analytical panel fits). Per-level `n` semantics are deliberately DIFFERENT: 'total''s `n` is the treated observations entering the support, while 'simple''s `n` stays treated+control units by contract - `n_kind` is per-container and each is documented, satisfying the never-conflate rule. (g) **MMM admission:** both `diff_diff.mmm` exporters accept the total container ALONE - `scale` (numeric or `"auto"`) is rejected as double-counting; for overall-total exports this route supersedes `scale="auto"` (see the MMM section). The total remains meaningful only for outcomes additive in levels - the same unverifiable caveat every scale route carries. **Reference implementation(s):** - R: `did::att_gt()` (Callaway & Sant'Anna's official package) @@ -6248,6 +6248,17 @@ ContinuousDiD, EfficientDiD): Scale by `sqrt(1 - f_h)` for FPC. Perturbation: `ATT_boot[b] = ATT + w_b^T @ psi_psu` where `psi_psu` are PSU-aggregated IF sums. - **Note:** When no strata/PSU/FPC, degenerates to standard unit-level multiplier bootstrap. +- **Note (weight-backend identity and post-fit replay):** the multiplier-weight generators + are NOT cross-backend reproducible: the Rust generator draws one base seed and row-seeds + Xoshiro256++ absolutely, while the NumPy fallback consumes the PCG64 stream directly, so + the SAME bit-generator state yields DIFFERENT (equally valid) draw matrices under the two + backends. `bootstrap_chunking.effective_weight_backend()` names the active one, and + CallawaySantAnna's post-fit bootstrap replay (row M-020) stamps it on the kit's + `BootstrapReplaySpec`, failing closed on a mismatch. Branches that never touch the Rust + generator — stratified/single-PSU survey generation and census-FPC zero weights — are + stamped `"portable"` and replay under either backend. Within one backend the chunked + weight stream remains bit-identical and replayable per column tile + (`ReplayableWeightStream`); downstream GEMMs match to BLAS reassociation (~1 ULP). **Rao-Wu Rescaled Bootstrap** (SunAbraham, TROP): diff --git a/docs/methodology/REPORTING.md b/docs/methodology/REPORTING.md index 2c7abf2f1..90965895d 100644 --- a/docs/methodology/REPORTING.md +++ b/docs/methodology/REPORTING.md @@ -38,10 +38,17 @@ kit is a panel-backed Theorem-3 recompute from the retained working panel, and TwoStageDiD's runs a fresh Stage-2 OLS + GMM sandwich over a retained frame copy (ledger rows M-021/M-022) — so those two producers DO recompute variance from their retained kits, exactly as -their own post-fit `aggregate()` does. Bootstrapped and kit-less fits -fail closed: the derivation exception is caught and surfaced as an -explicit per-check skip reason, never substituted with analytical -numbers. The raw field, when present — including the +their own post-fit `aggregate()` does. Bootstrapped CS fits derive +successfully too: `aggregate('event_study')` replays the fit-time +multiplier bootstrap (percentile inference; the derived container +carries `vcov=None`, so parallel_trends rides the Bonferroni fallback +and pretrends_power/sensitivity the diagonal-covariance fallback, and +the replay's re-emitted fit-time warnings are recorded and +republished per section). Kit-less/legacy-pickle fits, +backend-mismatched CS bootstrap replays, and the other estimators' +bootstrap gates still fail closed: the derivation exception is caught +and surfaced as an explicit per-check skip reason, never substituted +with analytical numbers. The raw field, when present — including the requested-but-empty `{}` sentinel, which encodes fit-time configuration such as a `balance_e=` that emptied the window — always takes precedence and is never re-derived. When the caller diff --git a/docs/migration-4.0.md b/docs/migration-4.0.md index 2c532db1c..8fd48b60f 100644 --- a/docs/migration-4.0.md +++ b/docs/migration-4.0.md @@ -155,14 +155,17 @@ surface (nothing to derive), and ChaisemartinDHaultfoeuille's pre-period checks read `placebo_event_study` directly. ```{warning} -**Bootstrapped fits have no route yet.** On `CallawaySantAnna`, `ImputationDiD`, `TwoStageDiD`, -`EfficientDiD` and `ContinuousDiD`, the post-fit recompute levels raise `NotImplementedError` -when the fit used `n_bootstrap > 0`, while the fit-time keyword they replace is removed at 4.0. -If you bootstrap *and* aggregate, keep the fit-time call for now and track the open -`TODO.md` draw-retention rows. `aggregate("simple")` — and, on its four adopters, -`aggregate("total")` (3.10) — does relay, and `StackedDiD`, -`ChaisemartinDHaultfoeuille` and `HeterogeneousAdoptionDiD` are unaffected — their `aggregate()` -is a pure view over stored fields. +**Bootstrapped fits: CallawaySantAnna is covered; the other recompute adopters are not yet.** +On `CallawaySantAnna`, the post-fit recompute levels now REPLAY the fit-time multiplier +bootstrap from the fit-retained RNG state (percentile inference matching a fit-time +aggregation to floating-point reassociation) — no fit-time keyword needed; only pre-replay +legacy pickles and artifacts moved across the Rust/NumPy weight backend fail closed with a +refit message. On `ImputationDiD`, `TwoStageDiD`, `EfficientDiD` and `ContinuousDiD`, the +recompute levels still raise `NotImplementedError` when the fit used `n_bootstrap > 0` — +keep the fit-time call there for now and track their open `TODO.md` rows. +`aggregate("simple")` — and, on its four adopters, `aggregate("total")` (3.10) — does relay, +and `StackedDiD`, `ChaisemartinDHaultfoeuille` and `HeterogeneousAdoptionDiD` are unaffected — +their `aggregate()` is a pure view over stored fields. ``` ## Results fields @@ -297,7 +300,7 @@ does not mean no action is required, so read the `Fix` cell. | Row | Group | Old | New | Fix | |---|---|---|---|---| -| M-020 | aggregate-postfit | `diff_diff:CallawaySantAnna.fit[aggregate]` | `diff_diff:CallawaySantAnnaResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | +| M-020 | aggregate-postfit | `diff_diff:CallawaySantAnna.fit[aggregate]` | `diff_diff:CallawaySantAnnaResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. Bootstrapped fits replay the fit-time bootstrap post-fit - see the aggregation section. | | M-021 | aggregate-postfit | `diff_diff:ImputationDiD.fit[aggregate]` | `diff_diff:ImputationDiDResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | | M-022 | aggregate-postfit | `diff_diff:TwoStageDiD.fit[aggregate]` | `diff_diff:TwoStageDiDResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | | M-023 | aggregate-postfit | `diff_diff:EfficientDiD.fit[aggregate]` | `diff_diff:EfficientDiDResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | @@ -305,7 +308,7 @@ does not mean no action is required, so read the `Fix` cell. | M-025 | aggregate-postfit | `diff_diff:ContinuousDiD.fit[aggregate]` | `diff_diff:ContinuousDiDResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | | M-026 | aggregate-postfit | `diff_diff:ChaisemartinDHaultfoeuille.fit[aggregate]` | `diff_diff:ChaisemartinDHaultfoeuilleResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. | | M-027 | aggregate-postfit | `diff_diff:HeterogeneousAdoptionDiD.fit[aggregate]` | `diff_diff:HeterogeneousAdoptionDiDResults.aggregate` | Move `aggregate=` off `fit()` onto post-fit `results.aggregate(...)`. | -| M-117 | aggregate-postfit | `diff_diff:CallawaySantAnna.fit[balance_e]` | `diff_diff:CallawaySantAnnaResults.aggregate[balance_e]` | Move `balance_e=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | +| M-117 | aggregate-postfit | `diff_diff:CallawaySantAnna.fit[balance_e]` | `diff_diff:CallawaySantAnnaResults.aggregate[balance_e]` | Move `balance_e=` off `fit()` onto post-fit `results.aggregate(...)`. Bootstrapped fits replay the fit-time bootstrap post-fit - see the aggregation section. | | M-118 | aggregate-postfit | `diff_diff:ImputationDiD.fit[balance_e]` | `diff_diff:ImputationDiDResults.aggregate[balance_e]` | Move `balance_e=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | | M-119 | aggregate-postfit | `diff_diff:TwoStageDiD.fit[balance_e]` | `diff_diff:TwoStageDiDResults.aggregate[balance_e]` | Move `balance_e=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | | M-120 | aggregate-postfit | `diff_diff:EfficientDiD.fit[balance_e]` | `diff_diff:EfficientDiDResults.aggregate[balance_e]` | Move `balance_e=` off `fit()` onto post-fit `results.aggregate(...)`. On a bootstrapped fit the recompute levels raise today - see the aggregation section. | diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index 8ad64bd41..b64157b93 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -209,20 +209,17 @@ Staggered Adoption Issues # Check cohort sizes print(data.groupby('first_treat')['unit_id'].nunique()) - # Use bootstrap for better inference. On a BOOTSTRAPPED fit, post-fit - # results.aggregate('event_study') raises - the percentile draws are not - # retained - so the deprecated fit-time aggregate= remains the documented - # route for this case until 4.0. (results.aggregate('simple') - and, - # where supported, results.aggregate('total') - relays the stored - # bootstrap inference.) + # Use bootstrap for better inference. Post-fit aggregation works on + # BOOTSTRAPPED fits too: results.aggregate('event_study') replays the + # fit-time multiplier bootstrap from the retained RNG state (percentile + # inference - no refit and no deprecated fit-time aggregate= needed). cs = CallawaySantAnna(n_bootstrap=999) results = cs.fit(data, outcome='y', unit='unit_id', - time='period', first_treat='first_treat', - aggregate='event_study') + time='period', first_treat='first_treat') # Access aggregated results - print(results.overall_att) # Overall ATT - print(results.event_study_effects) # Event study effects + print(results.overall_att) # Overall ATT + print(results.aggregate('event_study').to_dataframe()) # Event study Visualization Issues -------------------- diff --git a/docs/tutorials/02_staggered_did.ipynb b/docs/tutorials/02_staggered_did.ipynb index 051d558b0..c605038b8 100644 --- a/docs/tutorials/02_staggered_did.ipynb +++ b/docs/tutorials/02_staggered_did.ipynb @@ -34,10 +34,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:29.280981Z", - "iopub.status.busy": "2026-08-10T22:40:29.280582Z", - "iopub.status.idle": "2026-08-10T22:40:30.229004Z", - "shell.execute_reply": "2026-08-10T22:40:30.228503Z" + "iopub.execute_input": "2026-08-18T20:30:19.478101Z", + "iopub.status.busy": "2026-08-18T20:30:19.477801Z", + "iopub.status.idle": "2026-08-18T20:30:20.210166Z", + "shell.execute_reply": "2026-08-18T20:30:20.209696Z" } }, "outputs": [], @@ -71,10 +71,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.230171Z", - "iopub.status.busy": "2026-08-10T22:40:30.230074Z", - "iopub.status.idle": "2026-08-10T22:40:30.240045Z", - "shell.execute_reply": "2026-08-10T22:40:30.239670Z" + "iopub.execute_input": "2026-08-18T20:30:20.211418Z", + "iopub.status.busy": "2026-08-18T20:30:20.211311Z", + "iopub.status.idle": "2026-08-18T20:30:20.220876Z", + "shell.execute_reply": "2026-08-18T20:30:20.220530Z" } }, "outputs": [ @@ -269,10 +269,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.253550Z", - "iopub.status.busy": "2026-08-10T22:40:30.253470Z", - "iopub.status.idle": "2026-08-10T22:40:30.259638Z", - "shell.execute_reply": "2026-08-10T22:40:30.259271Z" + "iopub.execute_input": "2026-08-18T20:30:20.233728Z", + "iopub.status.busy": "2026-08-18T20:30:20.233641Z", + "iopub.status.idle": "2026-08-18T20:30:20.239470Z", + "shell.execute_reply": "2026-08-18T20:30:20.239093Z" } }, "outputs": [ @@ -329,10 +329,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.260495Z", - "iopub.status.busy": "2026-08-10T22:40:30.260427Z", - "iopub.status.idle": "2026-08-10T22:40:30.268213Z", - "shell.execute_reply": "2026-08-10T22:40:30.267864Z" + "iopub.execute_input": "2026-08-18T20:30:20.240455Z", + "iopub.status.busy": "2026-08-18T20:30:20.240388Z", + "iopub.status.idle": "2026-08-18T20:30:20.248978Z", + "shell.execute_reply": "2026-08-18T20:30:20.248603Z" } }, "outputs": [ @@ -348,14 +348,14 @@ "name": "stderr", "output_type": "stream", "text": [ - ".py:5: FutureWarning: TwoWayFixedEffects.fit(time=) is deprecated and will be removed in 4.0; use post= instead. From 4.0, time= means the event-study calendar column only.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51857/1122311195.py:5: FutureWarning: TwoWayFixedEffects.fit(time=) is deprecated and will be removed in 4.0; use post= instead. From 4.0, time= means the event-study calendar column only.\n", " results_twfe = twfe.fit(\n", - ".py:5: UserWarning: Staggered treatment timing detected: 2 treatment cohorts start treatment at different times. TWFE can be biased when treatment effects are heterogeneous across time. Consider using:\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51857/1122311195.py:5: UserWarning: Staggered treatment timing detected: 2 treatment cohorts start treatment at different times. TWFE can be biased when treatment effects are heterogeneous across time. Consider using:\n", " - CallawaySantAnna estimator for robust estimates\n", " - TwoWayFixedEffects.decompose() to diagnose the decomposition\n", " - BaconDecomposition().fit(...) to see weight on 'forbidden' comparisons\n", " results_twfe = twfe.fit(\n", - ".py:5: UserWarning: The 'period' column has 8 unique values. TwoWayFixedEffects expects a binary (0/1) post indicator. Multi-period time values produce 'treated * period_number' instead of 'treated * post_indicator', which may not estimate the standard DiD ATT. Consider creating a binary post column: df['post'] = (df['period'] >= cutoff).astype(int)\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51857/1122311195.py:5: UserWarning: The 'period' column has 8 unique values. TwoWayFixedEffects expects a binary (0/1) post indicator. Multi-period time values produce 'treated * period_number' instead of 'treated * post_indicator', which may not estimate the standard DiD ATT. Consider creating a binary post column: df['post'] = (df['period'] >= cutoff).astype(int)\n", " results_twfe = twfe.fit(\n" ] } @@ -398,10 +398,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.269018Z", - "iopub.status.busy": "2026-08-10T22:40:30.268957Z", - "iopub.status.idle": "2026-08-10T22:40:30.276882Z", - "shell.execute_reply": "2026-08-10T22:40:30.276558Z" + "iopub.execute_input": "2026-08-18T20:30:20.249936Z", + "iopub.status.busy": "2026-08-18T20:30:20.249875Z", + "iopub.status.idle": "2026-08-18T20:30:20.257870Z", + "shell.execute_reply": "2026-08-18T20:30:20.257487Z" } }, "outputs": [ @@ -469,16 +469,16 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.277814Z", - "iopub.status.busy": "2026-08-10T22:40:30.277758Z", - "iopub.status.idle": "2026-08-10T22:40:30.428030Z", - "shell.execute_reply": "2026-08-10T22:40:30.427663Z" + "iopub.execute_input": "2026-08-18T20:30:20.258891Z", + "iopub.status.busy": "2026-08-18T20:30:20.258839Z", + "iopub.status.idle": "2026-08-18T20:30:20.375319Z", + "shell.execute_reply": "2026-08-18T20:30:20.375004Z" } }, "outputs": [ { "data": { - "image/png": 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Ol5QBXXI/V6ayynCkQ6dtQHUicAvUcbpt5rfffiv1uG7d9m4593oTVf0h/XhKrMCVHlMjwLvNO9E03i3jJevqquGk+rDRSv7tUcOoJNW4EtWV0m30eh/VCVNj7IgjjnANSK8hGKtxLmpMaz4KtEbf1i+65V8Ns3idk+m9tE661UeBW93+XtayqLRAYWFhqUaJlsNroKkkQVnbVLdDlbVOFeU15LWMWh8Fm1VmQ3Vfo+k5qep1qsg+4O3fJWmZdLteMvuy3l/1anUbnGrMqWSBPl+dkOh2uYo2JJN97031eQIAUJPpt1JBPwVEFfzUreXRnXzpMd3mrgulKk2koFB0ey0VnZNVdjmXLFnifu9VQimaLqKLXlMycKvHYrVNYrU3SrYvtJ7RbeTyrmfJwGB0+1HroLICKhWx+eabuzqyKn+ViC7aR7eZyqK2Xbx2VXQbNV67MlZ7qyQFt6N521rz1jxVekGfpz5DlWBQcFxBWwUno3kJCPHoXEOJIOqYTecS+mwUHFVChNqg5V3fkuum/aqin3X0PHVBQCW99NkqeUVlGbQNoimIW3J9ve2mddB+rgB5ybJ9XqKNnidwi7qOwC1Qx6nGlq7IKkgZfaXZayxJdGaf19Dx6phG04+uAmnKvCxrGu8HvGTWoJfRGa289cgU8FJQTQE2XY32GsAlr5qXRQ3tkhkWsTpwK2mrrbaKNEaSWRYv+JgogzKZ7V7RDgaSoRMNb9285VUjXAHoktS4rOp1qmhNuujsao/eT/tosu+vz+zKK690NXJ1wqGO+pTpoqxpZRlURHV/ngAA1HTKTFVAVBfCdUE5OvNQzymjVs8rIKps15q2nAqMKaEhXrav2ifz5s0r9pja19H19D25ubm2KZVsu3vtXN1J5GVYqkMw1X9V+1rJISUziUtSnVTd8aVM5Hh9caiWq9pl+ldtK2W5llRyWSrTrlQSSzSvHafsXLUNVbdXNVqjM3MV1KxoFrMGvYcSJZ5//nlXq1b7gzJwy7O+m4rOBRSoVod7yjrWBY7obG9RIkd0B3wlt5vavDoHVf3bWGIlYQB1TXL3HQCotdSxk4K0ujoa78prdCNFP8i6LUe3Z0VTQ1SdcOlWnGSm0RVrZQno72hTp06t9BVgZRvo6rwaidHZFQpOS7KZiipvoHWJHuJlfkabM2dOpLfbZJZF89QtU+rVt2QjWFeydTtZMtt0U9HnoY7B1EOseibWVW99dupsoOT2UWcN6sArXdZJ2eLR+6/mrYCrerIt7/urka8yCWo0q0dnlRLxsmJLKuuEoDo/TwAAagMFQHUruhIQSmbUKiCkjEc9p3aZMltr2nLqdn5dgFYHZtFtLZXc0p1dsQK0aj/ozreSHZiq89PySrZEQfQFfo+C0GoLeckMKlWl4KtKCeiit7ZJWckQCjyq0y11WqWsy5KUHPDyyy+7tpPOZbS9tN5eO9yjgLkk6ggtWZMnTy61nqL2sdq8Ws+S5RTU5q/MXVJKNlAA94EHHii2rVOxvmVRRrHODxSQV5kElWkoSetecv9QW1fnE3qtAr3qQE9Z2tH7ueapNndlzwuB2oDALVDHqVGkQJsCXKqjFB1o09V51fbUrUwK1KkWlRpxp5xyimt46Qqrfox1JVU9marhoOeSmUaUifrhhx+6H2bvlh5dja8sXZlVI0c1pbwMAF2RVlBRStaZqkq6JUzrqe2qq9DJLot6odXVdDVORdtLdZ30GSgbI9ltWtX0mag8hOqx6rPxApL67HR1XfuHGlRanscff9ytp5dBkQ7rpKCtllsBVjXsddvZwoUL3bIl8/7KWlD2i2qF6fXeZ6wTLN2yFi8rVg13L6MgVtZvdX2eAADUFroIK2+//XbMwKweU1kn1QYtmQVYE5ZTQTCV5FL5Mq8MmTJslZmoQGWvXr1KzUv9D+jCsOqHeu2PV199NVJHtzzUllE7Rm0otVESeeyxx1xwWnTLvIKMSg7R8nvULlaZLLUNS9Y8jUd9JujW/zFjxhRL9lCgT205tfNuvPFG99ihhx7qgsHaXrrrTXReo3JXandWRSBTbV2vxJzuwrrvvvvcunTs2NGdJ2l5dIedeOW2dKFfGcblCd7edddd7q62RYsWRR7T/iPbbrttytY3Gfqc33vvPde+Lxm09ui8xyt58fHHH7t10bmE2sPqF0TnSMpM9vZZJTao7b3TTjvFLIsG1DWUSgDgftj1A6ofSNWy1Y+mGmsKUHXv3t39sEZ3RHXssce627rUMZd+iNWYU4NF9T+927+SmeaQQw5xDRl13qVBQczDDz885m0/5TqwZWTYnXfe6RoyarioIaxlUU0xNSxi1fStKDWavIwEBTDVcNLtPrpdysskSGZZ1NDRtlBgUdtLQUJtezWEvfkks00rS++h7SfK8FCDXbeqPffcc8WyQPXZqQGqBqkal7qVTfuMArNqZKXLOumCg+rFqpGr5VUgXbeYeSc7yby/6hzfeuut7iTE66hCnY1cffXVcd9X20zZAtqfdcKmjitibetN/XkCAFBbqV2qjE4FeXTreKyAqNopqgnqtW0qQu3ieLfqixIcEt32X9Hl1N1LqnGq9obeXwFZZZmqU6h4t997HaldfPHFrkNYzUN3gOk1uuBeHgcffLBL7tBFebVrS3YeFU3By2OOOcYtvwJ4aidecsklxaZRW0ztMrUtd9ttt6SWQdnJKj+gAOkZZ5zh2kpqcyrQp86rrr322khHX0qUeOaZZ1zQWm1RJVGoLav26IUXXmhVVWLuxBNPdG1K7Rc6P/LmrQQYJToooKySFWoD63NX1qiCqQpoJ0ttRJ0PaZ/QemleCmDqAr+ye1O1vsnQZ69zAa2r2rHxzjW1Lkp4UBKI9g3dvSYqnaGAuC5IaJ/Veunz1TZQZ9kAzHxhejcBUIKuFivYpR/ORIX6dfjQrUv6EVZjsqLTKKCngKcaHHo/9TCq91aQU4+rkRZd30jLpqCyGgde0FRXtZVJqdtsopdZGaMqeeBlQOpvNQb02ljzFhXW17J4tXpjUfCuZP1WNVbVUIt3ZTjRspTcXmpoe9OVZ5tWdJ20LiVrAGv+CrCqgZyIlkXTlLUPVOU6JbMPKOCqTHLduqUGr/breJ1BJLOf6j016PmS3wuvnnF0pxUK5OsWNp2wKeAba5qKrHuy+ygAADWZfuf1u5uoYzBvmuj2QPTvsNqU+g0u+XupNpgCb+ooKxFlCZas31qS2hbx2jeVXU6PlkEBUd1eHh3c9do+etxLsIh+T7XP1J5WnX6VS1CbKF6bWQFVtQejt4n+1nurPRgr+O3NS+0UrZe2V7zzB7V3FIDWxelEF78T0fIo0UPrm6gslXcuU7IdG2/dtf31GUa3w7xyE8r4VdasApS6uL7LLru49dTnFevOK7U5tS21jNomascpY1r7iaaP9f7xPke9Vp+j5pOog67yrq+Wz9sfy+LNI95+ru2kbaI73JR9G+2FF15wgWXVv9X20vdOyxivDIf2cX2+Wt6K1iEGaiMCtwCAWik6cAsAAFDbqaMudY6lO3lUf9S7fV63o+uuOmUxVhe1x5QJrLIQyQQM00nJwC3+n7KelZWtOwlLJntEB26jazsDKB9KJQAAAAAAUMOpFINqrKrzMt3p5N1ce9JJJ7lSBtVB/Vn897//dVmh+remBW0Rm0qhqYyFgrX3339/mXfoAag4Mm4BALVSvDIDAAAAtZnKkOm2dN1urlvc492angoqBaHSAl5JtJooXsmBukzbRPtYyfId0RSs1zSxSoQASB6BWwAAAAAAAABIM1z2AAAAAAAAAIA0Q+AWAAAAAAAAANJMneycrKioyHJzcy07O5taKwAAADWY6icWFBRYkyZN4tbZqw1ovwIAANS99mvtbd0moKDt7Nmzq3sxAAAAUEU6d+7sOuGprWi/AgAA1L32a50M3CrT1ttAqeoVMhwOu14VGzVq5Hr3RAW3Y2GhFXz4oRvP3mMP82Vm1rpNyb4C9pXKKQyG7d3JRW5874EZlhngmJvMcWXPPfe0JUuWuB6TP/jggzrzRSwMFdnLi79y44e22dEy/XWyaVSjf4PWr1/vLsh77bvaqjrar6gawWDQZsyYYT169LBAIMBmBfi+AFWG35iaqTzt1zp5duL3byztq0ZvgwYNUpICPX36dFu3bp0NHDiQBlslA7f+/Hw3Xq9Bg1obuC0sLHT7Zk05aUb1YF+JH7hdH9oYuG3QgMBtsvvK33//bfPnz3dBu1T8NqaLDaEiW5tZ6MbrN2hgWQRua+xvkNe+q61S3X5F1Z5Uiz43ArcA3xegKvEbU7Ml036tk4Hb6jhhXrRokatfoXEAAAAAAACgIuWT8vLyXHxJgVvFm+bOncvFwWoOwDZt2tQaNmxY5fMmcJsCyljp2rWrrV27tsZlr6Qdv98yBw2KjANAqcOEz2xIV39kHEgk4PPb9k37RsYBAACAdPTGG2/YuHHjXPmdaEoSrO0lo2pK8HarrbayY445xrbbbrsqmy+B2xR9eJtvvrm7KlLbb+Pb1HyBgGX061fdiwEgjQX8PtuyIzUEkeT+4vPb4MY92FwAAABIW08//bTdfffdtvPOO9uYMWOsZcuWLr6krNv8/HyrV68eiYLVqKioyObNm2fvvPOOnXPOOXbLLbfYjjvuWCXzJnALAAAAAAAApCH1l/Tggw/akUceaeeee26x5xS41fM1sX+C2mbo0KG2//7721lnnWX33ntvlQVuSf9MAX2RlLpOjdsq2JahkIWWLXODxgGgpFA4bEtXbxw0DiQSCodsUcEKN2gcAAAASCc//PCDbdiwwY444ojqXhSUQZ2QHn744a7j53/++ceqAhm3KRAKhez77793gdsRI0ZQLqEygkEreO89N1pv9Gjq3AIofZgImb01qciNjxmeYX6qJiCBonDIXlr8pRs/tcO+lkWdWwAAAKQRdT6mUgjt2rWLPKYsW92aH69UQk5OjsvCXbp0qXXv3r3Y/NSRmW7t79KlS7HHFWxs1qyZG9frYtEyNGrUyFasWGHLli2LOU39+vWtY8eOVlXWrFljK1eutA4dOiSVVayO21atWmXt27ePGX9bv369LV682Fq1ahWzMzG9n0qdbrbZZjFfr+c1fz2vQG1J6uNKlixZ4sqmVhaB2xTRzkXaOgAAAAAAAJIVDAYtI6N4+E4dlF1++eWRZEEFXdu0aeMCtrL99tvbbrvtZkcffbR9++23riauN+0hhxxia9eudZm8CsJ6wch99tnHlWTQvG699Vbr3LlzqWW57LLLbNiwYfbiiy/a/fffH3OaPn362M0331zpD1jLeuWVV9q7777rljMrK8tuu+02GzBgQMzpFcS+7rrr7JVXXolMr7rA/aL6SXrooYfcOrZo0cKWL19uo0ePtgsvvNA9V1hYaJdccol98sknLoCtv2+88UYbPnx4JFh+8cUX29dff21NmzZ12/DSSy+1Aw44oNhyeJ+VPreqQOA2BRSB32mnnVzEPlY0HgAAAAAAAEjGwIEDXUdYXo3bbbfd1tW/VY1Vj8orKOv2559/tj322MM9NmXKFMvOzrbGjRu7wK3uCpeJEye6eJXqtCpw27p1azf/RJQBW9Y0lfHcc8/ZpEmT7IsvvnDLq6CtOv767LPPYk6vgK2Crh9++KELYt9333129tlnu78VTP3oo49c7dknnnjCBg8ebLNnz7axY8fatGnTrFevXi4Y/dNPP9mnn37qArtPPvmkez8FvhUEvvPOO10WrQK3Cgxrftrm/fv3t27dum2y7UCNWwAAAAAAAKAWUbBx6623doFbj4KOyphVRu5XX30VeXzChAkumKmSC5uKar4qU7jkEK8W7G+//WYnnHCCC9qKgtMLFy50AelY3nzzTZdNrKCtnHzyya7EgoKx8vzzz9uBBx7o1lOULfzGG2+4oK0oYK1xBW1FCZirV692wVpv+Y877rhIlvLIkSNduQUFlzclMm4BAAAAAACAWma77bZzpQaiA7djxoxxdWhVViA6cOtl34rKBCioGk3lAZSJm2gaUeC0SZMmpR5X+QUFR0vaYost7I477ij1uMoUiIKvU6dOtdtvv92OOuooF5COZfr06W7dPMqyVXBWjyvo+8svv9i//vUvt9yqG6zljJ7XqFGjXLBXmbuqC6yyCspAVq1cUYmFaKrzq1rAVVnPNxYCtymguhx//vmnS1/fcsstKZcApMD48ePtxx9/jPv8Djvs4G4vqWq6Inf++efbPffc425BqYiXXnrJevbsuUmWzzNnzhx766233BVJFVWP9sEHH7gfuegf7upUFdu0tnxulRUKh2zRhpW2uGClrQttsAb+LGuT3cw2y2pe3YsGAAAAYBMEblViQPGogoIC+/333122rQKWyiT966+/XMmDX3/91a666qrI6xQsVRmAaKqBe8opp0T+VgdfJaeRs846y2WjlqR6sxXxwQcfuOCzsm29jr9KUskI1en1avx61PmYOitTB26qSTtz5ky7/vrrI+uoUghesLd3796unq0C2go8q9ypyiPE669K0+k1Q4YMsU2JwG0KaAdasGCB+5JoHKiLFDBauGFFzICRfxP34q4fpHHjxrkDsnebxaYyf/58+/LLjT3UV5QCgPvtt98mD9yqvo+uPurfaKoBpOBlugRuq2Kb1pbPrTLyAgX27uKf7cfV021dqCDyeAN/tm3TpJeFLFStywcAAACgaimxREFI1bZdtmyZKwXQvPnGpA2dtygDV48pm9YrGSCbosatSg0ogFqSyjNsvvnmcV83evRoNyhj9sgjj3QZsCpjEE3BVQ1KnIymvzMzMyOxuI8//thl1GobKNHr3//+t/Xt29eVlLjgggtcgNurYfv555/bGWec4WrtKgmzZCdoCnY//fTT5vdv2ngGgdsU0M6j9GxF9+NF6pEkv98yvB4EN/GXA1VnyYaV9vHyiXEDRiOab2Wts5pV6SbfZptt3CC6rUKB22OOOcb9uHgmT57sLqrooP3HH3/Yjjvu6IqK6+Cugue6jUPf3V122cUd7KNviVAh93nz5rlbTJS9633HX3jhBTeNethUNqtumyhrfuvXr7f333/fXfGLdWXSox+Rxx57zA499FBr27Zt5PFnn33W/eiqKLquIKp4uuavHxf9CCWiH3H9IGmZ4om3/ImWRz19ankSrXu87R8t1jZV75+JPreZM/+2cMPNbdC2O5vfl1Htn1s6WLphlT226AObV7TcfFb8d0jfyc9XTrG1wdKNqLog4PO745A3DgAAANQWikGpTIDq3M6aNctllHo0rnNB3X2ozNxNrbylEnTneocOHdy5m+j8tkePHq4jtZKBW2nZsqU7V4ymc0CdN2oe6qjtsMMOiwSuVQZB5606Rxw0aJAL2OoOTK+Grc7/9Lw6PPMCt0VFRXb55Ze7EhGqmevVw92UOENJAUXfddKvHW5TR+JrO18gYJkDBrhB46gZQduH579nX6z6pVjQNjpg9Mj89910qabAoW6T0I+EepRUIDIYDLqC47qNQwG5xx9/3B3cdduFqMfJPffc0wX0dMuFAne6ZUS3mMRS1vz0I3nwwQe7wKFu/VDxdQUlY9EPjbJh33777chjqqmjq326BeS7776zI444wq2LfhD1vs8880zCbaDyAMq4jXXls6zlj7U8ul3m2muvdctT1rrH2v5V8bmtWrXSvnjnSbvhgtG2ft3aav/c0iHb/eMVE232+sUJpysK182MWwVrt23S2w0EbgEAAFDbqDMyJTKp5ICSVzwqmaCOu15++eViAd1NRedWytAtOcQK2orKGLz00kuRv3XOqnMvr+ZsSUpmiu6ITRnGOl/caqut3N8KzupOy+jMWU2ju3IV4FbWcfQ5nZ5X4LdZs41JZuoUTRm4eo2SlVIRtBUybgFs2oDR8ok2J39jL4zxzM5fbJ+smGRHtFGGZGovbuhArFqv3lU3/aDpQPz666+7uj/Kujz22GPtkUcecT8cuoKnK2y6JV70vK7EKWiqUgy6hUM/LqeddporN1DW/HRrhX4kdLVO2ZwKMB5wwAFxl3fvvfd2t3eceOKJ7m8FInVLi2r9KFv0oIMOsksuucQ9t9dee7kM3ET0I3T44YfbAw884JanpFdffTXh8pdcHgVyveUpa91jbf+SFAAuuU1r4udWnVSiZPzqaUlPH6RkAgAAAFBrKECrcyFlkkaXdevXr5+rCavzHk1TXvE6JxNlxlaWkmOuv/56a9WqletI7NFHH3VB1n333dc9r6QaBVp192UgEHDnaxq6dOliffr0sbvuust23XVX19GYqD6v5qmAqzJpX3vtNTcPndOKyiaoAzS9h+7AVEmFVatWRc4hdZ6txB/NV3dxerR8XnB3UyBwmwKK0iudWoPGKZdQuW0ZXrXKjfuaNmVbpjkFjFQeIRnjc6fZzs0GWLvs1Fy18ugHIDpoqDqnyiRVD5LRvCt33g+aAnm6WqcasfpO6/sdS1nz+/7772333XeP3IKvoGd0baGS9KOiHwqvF8xPP/3U9X4pyux/4okn3I/vbrvt5q4oaiiLSh0oeKks3ZLF3sta/ujlUbkEXcX1lqes18ba/lXxuek4kV9oVhQ0dwU5HT636rSxrnTxbPeyLrjUJdpflhfmufEWmTn8rgAAAKDGUXAyXn8u7dq1cxm1qiOrTqg9uiN8//33d0HI6OxRBSEV/ExE06v8QKzOyaQ8tW/jUWJM/fr1XQBVd00q6HzTTTdFSieobu8NN9zgkmm07jr31d2kTz31lOvMTJ2GKUPWo79V6k/DG2+84Uo0vPjii5HzSgVuFYTVHaUK2Or8Tu+tddVdmApSqx7vRRddVGw59TolUG0qBG5TQIGCb775xnVOps5+KJdQCUVFVvC/27LrjR5tFlVvEjU7YKTpFm9YmfLAbckrYwpAluyJUgd43TYh6llSvW0qYKofDNVx1W318ToeLGt++gEoGbhUbZ549GOrK6PKclXgUPV71UuoKFtU2aFvvvmmq82jW0i0rKoBm4gCmJdeeqkruaAfsfIsf/TyqM7rpEmT7Oabb07qtbG2v97fK5mg1+qKaXk/t1DY7I8FIctuO8h22rJ5Wnxu1UmdAZZHXetCszActOcWferGT+2wr2VF1UUGAAAAagJljyZKEix5nufReWBJCuZqSER3bWrY1PbYYw83xKLknJIBYp37Jjr/HTx4sBviUTavl9Fb8vwzukRgKnF2AiBtAkbrgslnBW4qusKmHjTPPPPMmM/feOONrqi7btNXhquoVmpF56fgX8kC6goyJuKVJ9APs24B8Tpc00Whk046yQ1z5861hx9+2M466ywbP358pMRAPL1793ZXVUv+8JW1/NHLo/dX0XZveZJ5bVWJfq/CYNie/mZjJu1RwzPS5nOrLg38WeWani40AQAAACA90FNWKjay3+8i/uqxjmxb1CXlDRg1CCQOLqaCrtrpdn/VUPUoKOkFNNWzpYq7e8G/X375xdWR9W65V20dL9M+mfnp2KDnVehc5syZY7/99lvCZVQpAmW2KgiprFuPbvPQ8ohq8ugqqeYbrxxASWPHjnWdcHkdcCWz/CWXxyuTkOxrSzr++ONdsFSDl21bcpvGEuu9Zkz6xN57N30+t+rSJruZNfAn/91KdZ1pAAAAAInFu1MQtf+zIuM2BZQVp4CtBurboi7xAkbJlEvQdG2yNl1B72QdccQRrh7OIYcc4oKiylxVPVNlr8qee+7p6uao9ImCfp9//rkrF7B06VL3vLI0ld169dVX21FHHVXm/I4++mjXoZduM9Gt+J988ombR1n1XVVMXfVWVaw9+gdCnYQpA1bL8P7777vatergKxkqVq/6POeff36ko62ylr/k8qhEQ7LbMlklt2ks0e+124gR9t3kuTZn2g92wCMPpc3nVl02y2pu2zTuZR8v/f/awokEuKYLAAAApA31vbF+/XqXNKLSeEhv3p2YXi3eyiJwC2CTBoyGNu5pX6zamAWayDZNelnbTRS4VUBNRclLFmtXcfOSB1N1NvXcc8/ZZ5995jIy1UOlgnm6dV7UC2Xfvn3t119/dY8pS1XZnArsiWqgqhi6V56grPnp/V944QWXvam6qg888IAreq6M2URUAkHvG11XdfTo0S6AOmHCBBegVK1ZZfrH0qlTp2KF2j0qH6BeRb1yB2Utf8nliQ5elvXaWNs/lpLbtKzPbcaff1m3LbvYyH9dYYMHt02rz606KIN29+aD7K81821u0bK402WQaQsAAACkHZ3/6M7AH374ocz+S1D9vv32W3e+2LNnzyqZny9cB/Ot1fHNH3/84Wo66srFpqYvmHprX7t2revEx7slF+UXLiy0/BdeiHRO5quFnZPpK6krNE2aNKkVGdpLNqy0R+a/b7PzF8edpnO9NnZi+72sdRpk3NYktW1fqSrRNW7HDM+wzADbRvvKvDVL7Os1v9n41dOKZcEr210XTs4f/C9bOH+h69ROPcvWFRtCRfbAvLf/v3Myf92+pl0TjyupbtdVl7qynrWROsOcPHmyO/HmPADg+wJUpH2mO/5Uxu2WW25x/Zx47TQ9pzaC2gY1pe1WW4VCIfvqq6/s8ssvdx13X3HFFVXSrqvbZycpoi+SsrqUAaeML6AuUTBWQdlPlk+KGzAa0XwrgrbAJpYTzLbD2+xkOzcfYIs3rHSdAaqutEqUKNv9QkokAAAAAGlHAdk77rjDTjnlFDvmmGNcqTrdCahynIo3qRRcRkYGgdtqpM9g4cKFtnLlSnfX64UXXlhl8yZwm6IvmW6fVcYtV0Aqye+3jD59IuOoOcHbI9ruHDdgRGdIqEp+n1n/Dv7IOKK3jd/aZbdwAzYK+Pw2qHH3yDgAAACQbhSofemll1xZPJV3y8vLc0FbZXmqdFvbtm1dIBfVQ9t+2223daUslLBZlbE/Arcp+gBVH1G3HvJFqhxfIGCZgwdX0SeDVCJghFQJ+H02tBslaZDk/uLz2w5N+7O5AAAAkNaUVTts2DA3eCjHU/sRjgcAAAAAAACANEPGbQp46esaNE65hMpty/DatW7c17Ah2xJAzOPEmv+VUm6UvbFcDZDodyUvuM6N5wTo1AEAAABA+iDjNoU9y/34449uHJVQVGQFr73mBo0DQKnDRMhs3PgiN2gcSKQwHLQnFnzkBo0DAAAAQLogcAsAAAAAAAAAaYZSCSmgDsmGDx9O52QAAAAAAAAAkkLGbQqovqJ6/9NArUUgdVasWGGLFi2KOaxZs6bC8128eLEVFGwsorp06VLLz8+3dLN8+fJKraO23ffff1+ly7Qp5hnrPcb/8EOFX79hwwb3+QJAZX3yySd26KGHljnd+PHj7eCDD7YhQ4bYkUceadOmTWPjAwAAwCFwCyAlQuGwLcoN2a9zgzbh76D7V3/r8U3lggsusD333NMOO+ywUsNzzz1X4fnuvffe9t1337nxU045xZ10p5sTTzzRHnzwwQq//uqrr3a1uavSpphn7Pf4ssKv/+GHH9w+AwCV8dFHH9l5553nLgYlMn/+fPc7cvjhh9t7771n2267rZ1wwgmVuvAGAACA2oNSCSmgDsnmzJlja9eutZycHAsEAql4WyBtLMsL2TfTQzZpTtDWF/7/4/UzzbbqFLDhPf3WMmfTXEdSkPW6664rczqdXOfl5Vnz5s2LZcYvWbLEmjVrZoWFhZaVleUy56MpOKrvdTR911UipX79+sUeL2teubm5rof7pk2bFluulStXWps2bdzfen716tXWpEmTcm6J5Ndb81+/fr1bD2Ww6rnKrluseSZ6Tbz3SWa5161bZ+vyVliDnLKXO3r54j0HAOVxySWX2GeffWbDhg2zhQsXJpx23Lhx1r9/f3dBUc466yx7//333ZBMti4AAABqNwK3KaBAy+zZs92t1b169UrFWwJpFbR9/vsim7eidGatgrjf/RW0f1aE7MhhGZsseJvIN998YzfccIMrLaCSB9nZ2Xb55Zfbvvvu657Xv6pR/fXXX7sg4QcffFDs9bq99dJLL3VZmgqw6oRd2bi6QNO3b1+7/vrrbfPNN09qXq+99pq9+OKL9uGHH0Yee+aZZ+ydd96x119/3V599VW7+eabXaBSx5UDDzzQZWpV9Xo//vjjLotYgU4FVxWcruy6xZpnrNeU9T7JLvdm0xbbwWfca6tWrbT/XHZp3Pn9888/NnbsWJs5c6bVq1fPRo0aVaHtCQCenj17ujs+lEH78ssvJ9wwv/76q2255ZbFHlMgd+rUqXEDt8Fg0A0l6bdBx7/o6RKJTiSozdMqgUK/mVUxrbavd7GwvNNqiLfclZlvTZpW02n6eKL3YaYt/3Yo67tRU44Resz7vtSlYwTT8r2v6PEv+juTaNracoyIVtOPEckicJsC+mDatWvnssCocVtJfr9l9OwZGUd6UxkEZdrGCtpG0/PfzAjZfoN85o/Kdq0KCu6ppm1JLVu2dP/edNNNdtJJJ9n+++/vDuC61V7BUS9wKwsWLLBvv/22zFq2V155pcscVfBQ/9566612xhlnuKCrd0BPNC8FDbU8M2bMsB49erjHFMzU48ou/c9//uMCuzrJnzt3rh1//PG20047uVtry6OoqCjheiuQ+ffff1v79u3toosuqpJ1izXPWK9J9D76EUxmuTdr1852OvgCN79rrr4q4XLrNV27drWXXnrJZQKPGTOmXNsSNZ+OOVs26hIZByrr2GOPTXpaXazSnQfRGjdunLDW9s8//xyzsa8LYAr6enShK96Jie7sGDBgQORvXdzS3Q+x6K6SQYMGRf7W8TTe72HDhg1t8ODBkb9/+uknd8dDLLpYts0220T+njhxovutiyUzM9O22267yN9TpkyxVatWxZxWx3ZdFPQoCK7jezz6HfX8/vvvrnZ9PJqv95s3ffr0mO0LjzKu9bsjf/75pxt0d0escwFtB20P0YXEefPmxZ2vtq+2sygxRHf1xbPVVlu5/UnUbtBvZDzaH7w7flTC46+//oo7bb9+/axFixZuXNtA2yKePn36WKtWrdy4tq22caKLHm3btnXjukCrCxvxdO/e3bUpRPuC9ol49DvfsWNHN67PYNKkSXGn7dSpk3Xu3NmNa9/VPhxPhw4drFu3bm5c34lEpbN0LrjFFltE7hpKVPNf20DbQvQd1nc5Hm1bbWPPl1/GLxdVU44Rau95/Unoe1RXjhFqE8fDMWIjjhGxjxFKgtF3JtZvTG08RtSWdoR+w5NF4DYFFFVXEEa3QUdfwUD5+QIBy4z6ciK9LVkdduURkjFpdtCGdfdbmyZVGzj59NNPbcKECaUef/bZZ13W5dtvvx15TAdRHXjfeOONYtPuscce7mCvIR79WCpT9vnnn3ffdRk9erQ99dRT7sTD+2FLNC+VQ9APlWoj6pihEyf9UNx5552u3qF+ONWw04+vTkD0ft57lYdKEiSz3lW5bvFEvyaZ90lmuRV8226LgJvfRwnmp4bBb7/9Zvfdd597f21/1ZZURi7qjgxfwHZpPrC6FwN1lAIUJU+y9HeiC/0KaHkdZEbTyUr0CZbKNMTL5tDJXXRGiAIG8U7OdGIT3X7Vb1O8kzPdBRFd9kYByFjLKjruTp48OfK3AovxTuR0vC85rRIiYtGyRk+rZYh30ifR02o7xDvp8070vG2hgEyi3+Bffvklsi28QHy8gLx+673fTp3wJTpB1O+XtrMsW7bM7Q/x6DfOKwOkeSY6mdT7N2jQIHJBQXfHxKPPo1GjRm5c2yBRcErbS/uMaNsmmla859XuSTSt9m1vfbQvJJpW+7a3nVRSKdG02re9k3ntu4mm9co2ea9LNK32bW8/1AX0RNNqGTV465loWs0zupZ2omlr2jFCy1eXjhHxgkjCMeL/cYwofYxI9BtTm48RmTW8HaHfcC+ZrCwEbgFsMstWh4vVtE1E0y3NM2tTNaVbk65xqwaVbmVVg2jWrFnuylfJ2xu87I9E9DqdaJ9zzjnFHtfBOPpEqax5KbtWWbXKClUQc+DAgZGMEtU+vOKKK+zGG2+0HXbYwd1G27t3b6uIZNa7qtctlujXJPM+VbncGnSldrPNNiuWwQMAqaKMlZIn9mXVMR8xYkTMmtwlb3GMzpop69a+2jyt7lJJ9hbHsqat6K3NOkHV75YyVWP1dVFXbpnelOUPorOuypp26NChSU8bnflV1rTRGWVlTats6GSnjc5qSzRtWd+NmnKMUPBHFyj0ffEyUmv7MYJpOUZU5hih74r3nSn5G1MbjxG1pR2hC5SJ7m6JRuAWNYr7EnhXUerVo/REmks2aOvJL0xcUqGq6SqbOoTRbaq61f6YY45xVwTPPvvsYtOV7Ggr3gFb+6c6lPFuYYylrHmpVq4Czbr1UWUS9tlnn8hzp59+ussY/eqrr1xWrkolXHvtta7W7aZY76pet7JeU9b7JLvcmsf6DWErCvkTzu+LL75wJwfRGW/lqTWE2sHtL6GNmQj1/Vn8riCldPHtjz/+KPaYbrPTsT4eZZhEBzPiKU9nuEy7abeDd7Kmz62s19XmzwKbXjp8zpWdVm2zWN+XdF1epq267YCK8QKudf03JlDDpi3PXarct58C+vFRLRHV8iirCDLKUFRk+S+/7AaNI73VT/5Y5NTLTG19Sd2qoKzMRx55xI466iiXKaGrXomukiWqn6YgpOr7RPNqdCVLtYVUs1ZZpbrFUYFc0e0wup1Czx9wwAF2//33u6zbWGUgqmK9o2/Trap1K6vGd1nvk+xyh8LmOsSbuLxTwvkpYKLbFaNr6CWqvYfaqTActEfmv+cGjQOpdNBBB7lal2+99Za7ve/JJ590tzHqbhEAAACAwG2KKLBQkWAQUJO1bOxLOnir6VrlVP0yeJ2TlRxU60236evq5KuvvuoyXMeNG2cPPvigu6UxuhZQMhRQVYaUMmB1oWbatGl2yy23uIzZRDVxYtFrdPK+9dZbW+vWrSM1hnbbbTd78803XU0eFaFXPR1NE4/et+R667Fk1lsZqgqSaqiqdYueZ0W2YbLL/c+cObZi8RxrkNPMDjv8iLjzU01bdXKmTt9UZ0zTPPTQQ+X6rACgPHT81u15P/zwQ6Q8izpYvOOOO9zFqNdee80efvjhhKUSAAAAUHdUa6kE1XpSx0E6EdeJtLKtkqHsKN0qfNppp1XJ/DY1LY96jaRzMtQ1rRv7bKtOAfvur7Kz2LbqHLBWjas241aBQGW66/b6klR/5t5777WbbrrJdValDFd1+vX000+7WrIzZsxwdYIUOPU6AfEo4Of1/qyeOr1agxdddJGrV3jrrbe6QKJ6+3zuuefcckisecWy++672z333FOsBIKOZ3fffbc98cQTbrn1Pueee66rdRiL6rjqeKghmo6bRxxxRJnr/a9//csuvfRSu/zyy11HblWxbiXnGes1id5HQzLLfckll9qfT19pR17wpF1w4UXWonmzuMv93//+1wVMLrzwQve+Krvw6KOPlvkZAUBZdCGq5O+PeqFXD8nRt8fttddeblCpFjqxBQAAQDRfuJrSQD/++GN3oqzbXdXrmrLIlGGmjngS0Ym3ghkqOaD6jxWZn+okqp6YbpP1ek/d1LSZFbhVBkVZtwsjwXYsLLT8F15w4/VGjzZfOeqC1BS1bV9Zlhdyt63PWxH/UNOhuc+OHJZhLXO4CaAu7ytVpTAYtqe/2VhKZczwDMsMsG2S2VcUUFIvr+oMT72n1hUbQkX2wLy33fipHfa1LH/dLv9fE48r1dGuqw51ZT1rI5236C4ZnZdQ7xHg+wLwG4N15WjXZVTXScENN9xgF198sR1++OHuMWU/3XbbbfbMM88kfO19991nCxYscBlvVTE/AJuWgrEKyn4zI2STZgeLdVim8gjKtB3ew0/QFgAAAAAAoLoDt3///bfL6lG9Ro9u91WtQkWd40WbFY1+8cUX7cQTT3SdOFR2fqmiW99U00w1FXNycrjSjjoZvN1vkM+Gdffb0jyz/MKw64hMNW1VHsFfQ7K6AAAAAAAAanXgVhmzWVlZrgajZ7PNNnMBTnXA061bt1KvUc/fqo143nnnuV7CKzu/VHYYpuVQcFmdJG2xxRbUL6sE93n97zOLHq9NvP2ytnVm5/tfzdvWjb2//l9tW9dUqa37SlUfJ9g85d9X6tI+5baLRe8vdWfda8txpSYtKwAAAJD2gVsFMBVojeb9Ha8nd3UW07hxY9fJg3rcrez8ZM2aNa438lQEbpVpq2VavXo1GbeVEQwqKu9GC/LyzAIBq210AqpMcakp9QVRPdhXYguGzNr9r15y3uqQBSidnNS+4gW/vBqndUVROGidbOOF37zVqy3DV/t+V2r7caWgoKC6FwEAAACoPYFb9cBespG9fv1692/Dhg1LTT9z5kzXk/orr7xSJfPzNGrUKGVlFAYNGlTjOvtIW7vvbrWZFzxhXwH7SsXt2Yz9p7zHFe9x/avp6pIDmu5Q3YuQNmrib5AXaAYAAABqm2oJ3Hbq1Mllui5atMjatm3rHlMP1pmZmZG/o915552uszEFb0VlB5YvX27XXHONnXDCCeWen0cnJKk8KfHer6acCKH6sK+AfQXVeVzhd6puq2m/QTVlOQEAAIDyqpYbSDt27Gjdu3cvlkGr8gfDhw8vVfJA9t9/fzvqqKOsa9eubmjVqpWbTuP16tUr9/xQc7m6e4WFGwdq2gGIc5woDG4cOE4gmd+VDaEiN7C/AAAAALC6nnErV155pZ188sn266+/ulvclEX77LPPRp5/4403bNWqVXbsscfaiBEjir1W5Q2mTp1q//rXv5KeX3UKBoP23XffuVq8u+yyS6nO1VAORUWW/8ILbrTe6NFmmZlsPgDFDxMhs6e/KXLjY4ZnWGbdLlmKMhSGg/bAvLfd+Kkd9rUsH7/RAAAAANJDtZ2dDB061D744AMX0MzOzrYdd9zR1Zz1KKs2Xv3ZAQMG2GmnnVau+VW3oqIiF8AFULPoQlCqamEDAAAAAAB4qjWtpE2bNnbggQfGfG777beP+7pu3bq5oTzzq05+v98FllevXu3GAaTG9ddfb717967wceHhhx92mf8XXnihpZu7777b3WGgi0LK4o+u8bjnnntWeJ3POeccO+6442zLLbe0iy++2A499FDbeuutq3DJzW6++Wb3HrpAt2LFCrvnnnts2bJlbpl33XXXcs/vggsucOV0Bg4cGPP5ROvx22+/2UMPPeS2ZyxlPV9eN910k1v31q1bV8n8AAAAAAC1F/cDpoACKsrYUwdqdKCBuiocCllo6VILLV9ulp9vVq+e+Vu0MH+rVubbRBc0fvrpp0rVuVZgtH379paOpkyZ4upxHnTQQe74En1s2XzzzSs836+//tr222+/SH3xyswrlvfee88WL17sgrZy33332Y8//mhjx461Hj16VGieutNijz32iPt8ovVQR5da53jKer68FDy+6qqr7P7776+yeQIAAAAAaicCtwA2OQVrC7/7zgp/+WVj0NZTr55lbrmlZW63nQviptratWtdR4bTp093mbVdunRxtbOVvf/CCy/YxIkT7ffff3fTXnTRRe7fL774wt5//32XPa87A/bZZ5/I/M477zz390cffWRNmjRxmZ7R9F5//fVXsQze77//3t5880278cYbXUBTtbnnzp1rHTp0cPPq1atX3OXfbLPNXCeMeq9YF4USrV8yy6vlUqaqF2StzLqLsoOVbasMVrn33nvt008/dePqXFJ/y+eff+4eV5kKlb1R4NVbv3jvs2bNGrvzzjtt1qxZNnSbbc3aHxJzPRTsfvnll912b9u2rfXv37/YMpb1fFnbQdm/RxxxhFuHOXPmuGC0Mmy90j2q2X7XXXfZhAkTbMiQIXE/WwAAAAAAuG8/BUKhkM2fP98WLVrkxoG6FrQtePllK/zxx+JBW8nPd4/reZeJm2Lq0PDLL7+07bbbznUcqGDgueee654bPHiwdezY0QVOvQ4SFcy9/PLLXTBPzyvQqHIMnm+++cauuOIKVxolVsBV83v66add2RSPApaZmZmuBvaYMWMsNzfX9tprLxcUVABQx41NsX7JLK8yWZcuXVol6y4KdiqA2bNnT/e3grJe6RutayAQsDvuuMMFYxXw1Lxuu+02V16grPe55ppr3F0Nmuejjzxsn754Q8z1UOBYWb7aJvXr13cdW0Yr6/mytoPe6/TTT3e11nfaaSfX0WbJeSg7+PHHH0/wyQEAAAAAQMZtSiiD688//7SCggLr3r07+x3qVHkEZdoG589POJ2eL/z+e8saNWqTlU2Ilf15/PHHu6xHLxuyYcOGkQzOLbbYwmVoqlSCbm9fv369y+i8/fbbIzW49VoF4Y455phISQWVLohXX1aBvhYtWthnn31mBxxwgDsmKDNTgUJl2c6ePdueeeYZV/9U8912221dMDOeb7/91hYuXFiqxu3VV19tLVu2TLh+nkTL66mKdZdPPvnEBTM9qqOrrFZRYFllCR577DEXDN15553d46pRPHr0aFfDVoHveO+z7777umxX2bxzFxvzr6Nt6J7Hm1m7yDTKOlbg/NFHH7Vhw4ZF1u3FF19M6vlkt4MycM8888zINr/00kuLLavWTVnH+fn5Vq9evYTbHgAAAABQd1EqIQUUUFEASLf9UuO2kvx+C3TqFBlHelNNW1ceIQmFU6ZYxtChFkhRp00KdipYuHLlSndbvG6x1y3wynyN5e+//3aBPWVKKrjn0XdapQi8oJ3KEcSjaZVNq8xXBW5VO1X1abfZZhv3vsoy1S39o0aNcsumwG2iDg27du0as8Zt48aNk16/RMtblesuU6dOjQRk4z2vC1077LBD5LEBAwa49VG9YS9wG+t9ogPCW221lWVkZpqtmmY+3/8HbjUPzV/Zuh69lxeYLev5ZLdD3759I895x/5ouiiwYcMGmzFjhgteo3r5fT7r3qBdZBwAAAAA0gWB2xRQ4EUn8roFOlEQBmXzBQKWFRWgQXqLdESWjPx8Cy9bZpaiwK0oe1IZnp07d3aZnerASkHOWBQAVYDuyCOPLPY91i3+eq3Hy26NZ++993Z1ZhXMU+mAPffc081Pg+rbvvTSSy4j98knn3QBykceeSRuB2ll1bhNZv3KWt6qXHfNp2nTpnGfVzBT5QlKZhlrvgqYJnqfnJycyHiG32eNcxpaj1YFbtyjjFmVMIiev4LCyT6f7HbQOiSi99A0yjBG9cvwBWzvlttU92IAAAAAQCkEbgFsOskGbf8nXFBgqTJt2jR74IEHXLDU6yTq3XffjWRXltSuXTuXjangpzImK0q1UdU5mEokKANWgVmPArAnnXSSG9RRmUodPPXUU6Vutd8U65dIVa27ygKoPEA8ClDn5eW5AGmzZs3cYyonsWTJkkiHavFoe0V3yrZixYpSAW+VZVDAPHr+0TWEy3q+qraD5uEFqQEAAAAAiIf0TwCbTjnrd/qysy1VvAxOL7ingKFugVftW486DVOHV6LsV2XO33PPPZFyAzNnzrRTTjnFFixYUK73VimEu+++22Vz6rZ+L9B69tlnu6Cjd4u9AnvRGZ9VvX7Jqqp1V4ZwdIC1JHU2po7K7r///shjqjer7aByEokoU9lbt4cfftg6depk/fr1KzaN1kGPa56iz/a5555L+vmq2g7qKE2v1/YAAAAAACAeMm5TQCfo48ePd5lm6vFctSdRMeHCQst/4QU3Xm/0aPOpjiXSlr9Fi43B22Qyb+vVM1/LllW+DB9++KHrHDDaIYcc4mqtqpapasQqYPjXX3/Zrrvu6uqcqqMw1VNVUPWOO+5wgba77rrLrr/+ejv11FNdZ1TqZEw1SseMGeMyMctDnVcpG1YZtV6JA3VcqGPFyJEjrWfPnm4ZFLRVWYXydk6meY0dO7bM9UuW5l0V677ddtvZ5MmT4z6vEgU33nijnXbaafbdd9+5wLm2vYLc6uQrkaysLBcQVxmF+fPn294n32NPfhOyMcP///qkyhvcdtttLtCq+SuYrezZZJ+vqu2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rariuWCuzNrr0gOiKd/R7erVIdTW4sEyM6MyKE0880d2Kr+HNN990JSR0O5BKC3hX4Fu0aOGyepVRoMxOZd4qk1bZF96y6Iqzrix729mjEgG6Uq1b0FQm4cgjj8y3rCpjoDIAsR1fRG8vb522bt1qO8pbZ6/8g7KFtY21bt520W1ruqXLK3ehz1efrW4J82ia119/3WXAesusTI9oeqx1jO6xVp9tdCch3j6g29k86uRDNm7cmC/TRfudRxkU2jbKYBEtg7ZfdMdi2r+1DMrS2bJli7vtT/uC9mdlFiu7QRkyWocd6bCvpN8PT3RNMW/fLMtnCQBAdaB2YJ06dSLZrMpk9TJq1d5QRq7aK16bRJmvXp1Zj9q4XjsueojuwMzrnKyw6TQMHjy4yOXUHVgqgaD2ibIJ1S715q9lVLvDazupjeGVSVDbQ7fm61b46Pao1kFtIrUnC2vDq5RVbDs9tq3liW4jKfNU7avodlRJaRnVKZxHbT+tl9fe0q31art8/vnn7rH+6nFRHYupLVjSMl9qR+supdi2lWqzqt1XVDvZu+uwuDalRNdMVhtQ7XZlfSv7uTTnHpq2KMq41nmFspZ1x5n2G2XTen1naH20Dnqv2PMxbQe9pye2Nq3akl7G647S3ZI6Z4vO7vXKcyg73KOs7+jvnDLN9djbL3QHm0o46DXe/q02ufbL2PJ5QE1G52RADaYeWtXIiq01pFuM1BD1KBip+kOiGpx6XWxwVQ1aUeDUq9NZWEcBes4LrnrziqZGXuxzEh1Y80Q3EAujjgx0u5NuldP7qkaUGqXe+3sKq5vlBba929GiKQjr3cKmQKACv9EUCFQDWbfdqyErath4t/OrMagGW3TDRsso8UpWaJnVaC9um3qi5+2tU1G38hXHu1XMCxxqeb0Af2HUINQ0Wldve8by9hNv34ndB6LXqbDPvyT7gALLsbSNdTuftwzxtqm2l26tVMNYtxA+//zzbp/SuP6vW+p0u1dplfT7Udj+6W3LsnyWAABUB2ovKOijwK1uz9aFUdU39Sg4qtuzFVRTQDS2TIIo8BvbjovXaVNJpiuMgrOiW8vV5tZ7esE0tW8UgNbFarUtVcrJC+oqaKi2qMo0aYiVnp5e4DmvrmxsezJe512FtYF3pI0R25bTfNSW8wKf7dq1c5+VyhkoWKu/ClAX1k7ztG7d2gVFi6J2tc5bvHZ0vLZVbB8MhbUrY7dFYdTuj6btrO2l9VTbu6TnHsW1X1WTVm1unT+orwgNao+qtIVKRKgtGXsRInr9owPO5fUZF1YuQcun75bOCxWsVrmDkrTDvbawtpP6fNAQqzp2uAfsKAK3QA2nK+KqdaQAldeIiW2celedvcxH1QnVFfDo4K1XM1SNNK9zLtVfUk3TaLoK7DU0NN/Y4vhqSMR2YLYjVFdVdbauueYa15j3fvxVkF+ZqyXlZWJEU02romi76IryF1984R6rQaMaVQryKYPWa8SoVpVXM1aZsxLd2YRoW6uxqcaaruwX1pmAtmn0Z5QI3lVvZZ96V9rVAFetrMJoH9I0apDpM43OTNX66DlvP9HyR2cMKLtY611YAL+0ojuD8Ggbeic0WoZ421S8ZVBmuAYFpJUdoOC9agwrOyNe7bV4Svr9AAAAxQdFX3jhBZfBp7ap17+AKFCrNoUuwisAqPr1lUFtUK9jK9XW1zJH322m5dTdVVpOtQcV7BMFeNV+Uo3bwjJmCws0ehfYY+uDJrpeqBc49eizUBssOoCsrFvV6FUNWGVCK8GhKGp36RxF7fbCguaql6ogsGrZalqJ16YrjzaleAFZj95P7X61w8vr3MNrR1944YVuUAa1toPq4ao2r2rmqi3ptVWLar8mks4DlNGs/kUUDFabNja7N147XK8TnSsogO91aBatsE6zgZqKUglADacOnnRbiorGx97GI8pSiO7ZUz/Smj62F1XvVhmVA1AwSw2Ojz76KN80CmCq8aHbZkSNaxXej75dRx0RqJFdVgqIqvGrkgVew0m3vev5eCUiCqNgthqL0UO87E+Pll/BSWUXeBkWes8RI0ZEgrZq0HrBUP1PjR01srwOITzqyEGfkeaphrw6f4i+bUxX8NUBRLyOHcqDgvrKINBJh/fZaT/QbWkKYkdvGy2vAt1qxOo2Jy23PmOPArZqYD/11FNuHhLda6/3WOtYHuukq//RHXmtXLnSfR7eiZ0C0d6FC4/eW8ug9dF+rOwBnWxo2XWSpIxqr/M67c+FiZdlLCX9fgAAgKIp6KPfTrVL1U6KDlgpY1PtFLVL9NvuZb5W1nIqeOyVW4qmx8rE1a31WkYvMULtTSUC6Lb36LbW7rvv7jqjii4pFR24VVDMK0ng+eyzz3ZouYtqz0RT2QavkzhRh6t67N2hJuokTe0oZY0qKK2OaYui2/6VyauOaGM7YtPnqcCvAuAqI6bPWdPGtq10DqNM7PJqW3lJGaJ2obar2qtq15XXuYfWVdtqzJgxkf1YnfcqeO+1O9V+9TK4Y8/HtB28c5BE0kUFBai1Tb766qt8ZSQ8Wvfo4K3KkKlsnNcO17mALmboTkRv/9b3WHe5xe7DQE3GZQyghlMwTj3IKpimH1/d9qLn1NhSg0CNXV0ZVSNEVMNUjTAFehUE0+06yhDQLVz6wd5tt93cdAo2qsSCGlQKdOlH+uGHH3b/937YVatJP/bKQtX8lW2qHnOjsxB2lLIgX3vtNXflW++vjGDVmdK6eBmP5UElBNQg9ChbWPWtFNT0Mgm8jMw77rjDBQA1jXqTVe/GohpfapwrsKtplJ2gTGjN45FHHnGNNS2zrrJrW2nb6lZABUV1274C7tqW5UGNJ+/Wu+zsbHeyoHq8anTp8/MyZ5X9oSCt/qoHX50o6TYn1ePSviSqw6VMYfVsrF53lUmq1yiQqlu+vH1B66jgvRqhyp5QT73ax7zsibJQo1olDdTjrk6ENG9tS6/EgerUKrCschjartr3vFpizz77rJtGJ1EKXGs9dBKh7a7/Kbsi3kmgGu66dVMZQNH140SvK8n3AwAAFK1jx44uA1IXXFUPNJaCoqqtr4CQd3fTjlBbK7q9F0tt56JutVd7Qe0ptSFi2zdaNrVNFPxS+zralVde6doMagOqDaKApQJ6yt696KKLCryP2mmXXnqpXX311Xbrrbe6Gqxqb6rNUZpAbLz2TLza/sr0VDtW7StdNFd5MGUSR2dAa/so+PjGG2/YKaec4oKdRVE2ptrxaqsNHz7c/va3v1n79u1d21vtaAXC1feAlxShbaU2qLet1Hb12n2FZXTuCN0tp/axAsVjx451bVplfJfnuYdKLuiOPy272onat3ROoBqyCuiK1kdBWrXDtX3UtlT5Cd0Vds8995T6c95ROnfURQQprNaz2vc6x1PmsILYDz30kPvOerWItQ+ffPLJdsEFF7h9Qucg2j90fqjzAwDbEbgF4BoBurqpxoYCtbp6q4CXAm0qMq8fVDWURA02ZUvqx1RXQxVsVQdQaixFN4rUeFNDWkEw/QCrQaGOEhTA8+o6aZ76vxo4CqwpYKlMRj0uKwW/FAx7++23XSBVjTrVm1XA8+abb3YNrejOrnaU1zGFt22UQaAGiQLQygAQBSFvueUWF/xTRoi2i55Tg0wBV12N1rIpQKtto0aetpmyJnQS4p2IqPGreWjba3urwatGtDJClX1RHhQ49qixqFpeOtlQgyr66r22p06E1GBW5oQasfo87777bhf8FwVKFdBXAFtBSTXe1PjUCYcXzNb0mq8+J02r91MQVQ258mh0KktBgWU1YvX+ynh54oknIqUltN20f+gEQ419fYZaNpVC8AKu+my0Dlpur0MyZVdomnglKhQs1i1t+uwKq9tVku8HAAAontopyrSMzWQVPaeAqdfh145SYPKkk06K+38FzYrqcMorNaU2T2xJJLWXtA7KUo2tw6vlV7tQbUYFZNU2U1BP7cHY29I9xx57rEsK0OvUvlJbRzVTNZS2jRHbnlG7qjBqX+suMLVr1T7VMqhkQGygVxf11e5RwK8ktP4KkKoNpvMPBUHVZtJ5i+YT3aGY5ql2uKbTcigpQkFytZlja/DuKLV5NX9d4Fc2tJbLay+W57mH2uM6l9D8te/pHEnta5VdEK2PztvUDlfpLl0QUDKNPquBAwdaRdE66n3Vpi2snq22jfZt7XuixBSVWfOC9nqtgvAK6Op5nX/qPEoXGipyPYBk5wvTwwkAoBpShqyywZXBAgAAUBMoiK2gYnQdfZXV0kV43fmkYFllURawsoUV6K5KVINYF/i//PJLl7CC7XT3pbKLlVQSW/rCu7tNF04AlA0ZtwAAAAAAVAO6hV4ZjLqLp1WrVrZw4UIXWFM90coK2uouJZXfUkktlWhD1abSZgpiK0tcd9wpkxZA4hC4BQAAAACgGlAJLd1CrwCpSprpNnaVY1KphcqiTtnUAfGZZ54ZqW+Kqksl0lSuQ+URVG6somrqAjUVpRIAAAAAAAAAIMlwaQQAAAAAAAAAkgyBWwAAAAAAAABIMgRuAQAAAAAAACDJ1MjOyfLy8iwzM9PS09MppA0AAFBFhUIh10lKgwYNLCWlejdrab8CAADUvDZs9W7hxqGg7YIFCyp7MQAAAFAO2rdvb02aNKnW25L2KwAAQM1rw9bIwK0ybb0NVKtWrYRH0RcuXGhZWVnWsWNHCwQCCX2/6i6cm2vZn37qxtMPP9x8qalWnYTDYdu8ebPVrVvXfD5fZS8Okhj7Sny5wbB9PDXPjR/dI8VSA3yXSrK/HHHEEbZq1Spr3ry5jR8/3mqK3FCejV35nRsf3uJAS/XXyKZRlT22bNu2zV2M99p21VlFtl9RvoLBoP3555+cCwB8X4Byx29M1VSaNmyNPDvx+7eX9lWjt3bt2gn/EulEWCnQ3bt3r/a38VVE4NafleXGM2rXrpaB29zcXLdfVoUTZlQe9pWiA7fbQtsDt7VrE7gt6f4yb948W7p0qQvaJfq3MZnkhPJsS2quG69Vu7alEbitkr9DXtuuOqvI9ivK/3xA9LmRxAHwfQH4jUFp2rBEERNMJz277LKLbdmypUqdACUtv99Se/WKjANAgcOEz2zvXfyRcaAoAZ/f9m/YOTIOAAAAVAVbt261+fPn25w5c1wAkIuDlUfxPpU8aN26dbnPm8BtgunLs/POO7u6ZDUhGyTRfIGApXTpUtmLASCJBfw+69aWsjQo4f7i81uf+h3ZXAAAAKgS1qxZY/fdd5/9+OOPlpOT44a0tLTKXiyYucTNv//973b44YeX2/YgcAsAAAAAAAAkOSUFnn/++a5G6kUXXWQ9e/Z0ZdFUSom7vCu3LJLKzr3//vt20003uc9EfYiUBwK3CaYPS/VtNWicL1IZt2coZOF169y4r3Fj85HFDCBGKBy2tZu2jzepp3IJ1EtAfKFwyFblbHDjzdMamp9yCQAAAEhSn376qQsQjh071t3drTiTSiZUtf4JqqPdd9/dDjzwQLv22mvt6aefdlm35fGZcO9+goVCIZswYYJNmTLFjaOMgkHLHjfODRoHgAKHiZDZB1Py3KBxoCh54ZC9sfJbN2gcAAAASFbffvut9e3b1wVtkXxUInXo0KG2aNEiW7hwYfnMs1zmgiIpws6VDwAAAAAAAOyotWvXErRNcl5QXbWIywOB2wRTr34HHXSQ9evXjx7+AAAAAAAAsEN0J7fiTNFuvfVW23PPPW2PPfYodHj22Wfd32XLluV7nW7l79q1qyvt6cnNzbVu3brZF198YT///HPceapUgxxyyCFxp9Hry8PChQtdh1+q5ztgwAC3PkX5/fffbfjw4da9e3c7/vjjbebMmfn+P378eLfuPXr0sHPOOceVnvAsX77cLrjgAuvVq5dbt+effz7fa7/55hsbPHiwW5Zjjz3WvvzyywLv730+5XXXPTVuAQAAAAAAgCro6quvdnVVdaf3uHHjbMyYMfbWW29F/t+gQQN77LHHbPr06da6dWv33MqVK13Asm7duq605z777OOe/+OPPywnJ8f69Oljs2fPds/98MMPBd6zXr16kfEbbrjBjjrqqALT6H3LKhQKuc7YFGB+9913XRD3yiuvtBYtWrjAaSzV+9X0+t99991nr732mgvEfv75564O8K+//mpXXXWV3Xzzza7kxKhRo9z83njjDff6yy+/3G2jd955x+bOneu27U477WSHHXaYzZo1yy655BK3rZWgqe1y2WWXuW2twHmikHELAAAAAAAAVEEKojZr1swNGlfGp/dYQ1pamssuVeDWo2zYLl26uOBldGbs1KlTrWPHjtawYcPIc9Hz8oaMjIxC3z/2fctqzZo11qlTJ7vtttusffv2LmC677772uTJkwudXoHr9PR0F1zddddd7cYbb7Q6deq4LFtRUPu4446zk08+2XbZZRf3/9WrV9u6dessMzPTrf+FF17o3uvQQw+1Aw44wPVbJR999JELcJ9xxhnWrl07O+2009zd9Z988oklEoHbBNPVgT///NPmz59P52QAAAAAAACoUMqgnTFjRuSxgrUK2mr46aefIs9PmzbNPZcoS5YsiVtaQVmusZo3b27//ve/XWZwOBx2AdtJkybFXUYtf+/evSP9TOmvyh4oICsTJ0502bOetm3b2ldffWWNGzd2wehatWq55VDJiHnz5rkMXQWORZ2OKQM31qZNmyyRCNwmmHYs1RFRGrrGAVSs66+/Pu4PQ3nW3Ym2ePFi19vnjjr99NPt0UcftURSvZ5TTjmlwHHJq2OULHR186+//kra+SdqH0oEn99ny7LX2q8b59gPG35zf5dmr7FQuHxqLwEAAABI3sDtb7/9FkkojA7cKqCrEgOisgmJDNy2atXKlRgobCis3ELsOeypp57q6suqRm1hlD2rYG+0Jk2a2IoVK2zjxo0uqzYYDLqaufvvv7/LrlW8TpSpe8stt7iyCaqPe+SRR9qBBx7o6uWKMnijSyLMmTPHnU8qAziRqHGbYIruK8V6y5YtkYg/ysDvt5Tu3SPjqHoUJFqes85WZq+3raEcq+1PsxbpjaxVWmPz+8r/M9WtD6phI/Hq/ZQ31fjRj51u40hmunr49ttv2wknnGDJ6qyzzrIXX3zR/UiWlN9n1rOdPzJe3vOvilbnbLBPMifa5K1zbVsoJ/J8bX+69Wuwpx3auKfVVAGf320DbxwAAACoblQqQbVrlbSi0gEKZCoTVRmmKnWgc0MFJZV4qCBvNAVKo6mEwD//+c98naPdeeed+aZRndiPP/64wHJ4ZRx2xCOPPOJKJ6hswr333ms33XRTgWm2bdtWoESDHmvdveD0XXfdZVdccYWrT/vwww+7GrjKsvX7/W77HHzwwXb22We7wKzWS4FZlVeIptIKI0aMcNtw4MCBlkgEbhNMH7wCt4rqaxxl4wsELNUL3KLKWZWz3j5f+6tN3DjbtoayCw0eNU9rVK7vqR8hr3B6dL0fmCuy/sADD7gf3ugaRlVdwO+zXu3z97Ra0793Ty8dZ3M3L7WU1BTz2f9Hs/U9/Hr9NJu/bYWFrGZm3ipYu0+D7bc/AQAAANWRsklV01Z1bhWb0rg665K9997bBW6zsrJst912c2UDor333nv5Hnuv81x66aU2aNCgfM+lpBQeblRg+Oijjy70f7fffnuBAGk0dVAm2dnZkQ7ZYoO0Wk8FaaPpscogKBYgyqAdMmSIG9f5sDJvVUpB81WSl+6e1fR6P2XjPvHEE/mWS8FjBXZ196qCyYmO9RFJBFChwaNvNkzPF7SNDh49s/QTN11F8mrsqJdN/WDdcccd7nn1OqlbNXSLhDJSVQvHs3nzZhs5cqS78qYfvCOOOMK++OKLSGkGTTt69GhX8kCWL19u//jHP9y8dHuH/qfbMzx6L93qoaugev/o/0X77rvv3Dx0FdGjW0p0lU8/surlUkXWNY2KqOt9iqLbQ/SDpB+reHQ7yTXXXOPeo3///u6Ko95LTjzxRPdDFU3v//jjj7tx1ffWNujWrZtbv1deeSUynUpBXHTRRa6gu7KTo7evR9tKVPxd0+sqqOZ/8cUXu7pFH3zwgfux1GenZdOVYW1nNQY86glU66mrxPrh1a01XmmE2PnLL7/8YsOGDXPLrJ5IP/3003zLpG2qz11F6MeOHWtVIcNdF0sWZG+//SeeBVkrLSuUW2HLBQAAAKBi6Xx35syZ7txL5zMenY8pcKn6sJomljriih5iE6FUiiB2GiUJFUZlDBQILmzwzs+irVmzJnKu7VFwWTVodV4eq0WLFu41sfPQ+zZq1MhSU1Ndp2QePackJmUga9to2aM7Xttrr73ynV8qkKtzWAWDdedmbJA7EQjcJpiCCnl5eW6gxm35bM/Q+vVuYHtWHV7waGHWqmKDR1+sm1IpNTe9sgEK4ikAet1117l6NwoO6uraeeedZwsXLnTT3n333a7DQZVdUM+SChiqJIMO3vqrIOE555zjgoHaTy+55BL3Y/buu++6Wzo+/PBDe/LJJyOBxcsvv9zVm9X761gRr4fM/fbbz93KogCu57PPPnM/cPpx0RVHFU7XMmkZn3322SJr7WpeWl5dVVQto8Lo/yq2/tprr7mArOofecFtBbYVdI7+EdMPvq6gKrirbeYFWLU99froq7VffvmlHXPMMfbCCy+4QGksr6SFtqO2p2g59UP95ptvumDtyy+/7Lbngw8+6GoRaTtr2tUbcmztpqAL5KrR8P7779vrr7/uguLebT2x81c9JN0mo8Ct5nnuuee6QLyCuaL568f5nnvuseeff959XslOZUmU4V4SOaE8q4n0HV2Ts9EN/K4AAACgutJ56x9//OECtLGBWyXd6FwukfVtvUzc2CCvN6gDssISrS655JJIHVpRgFUB08KCpkpi0jmj167XX53r63m9d+fOnd35fnTJg/Xr17tzRgV3dc4fnbGrDsratGnjxlVqQeeIyrDVeaiCxBWBwG2CqfCzMuJ04u8VgUYZ5OVZ9ocfukHjqBpKEzz6OXOWrajgrFs588wzbeedd3alTZ577jmXTaqMS/2AKJirouQKXoqXmasgqaZX0G/Dhg22du1aV45BV/F0+4iu3KmHTl2hU6aqruzpB1JBTAUARcE//YCq1qrqrN58880Fiql79EOjW1AUrBUFIRX89Iq4L1261L2nfnS0vP/5z3/cFcKiqEdN1eJVnaDYTN9Fixa5q5sKdCorWcFVrYcC0Armqli7As8LFixw02u59H7aZgp8KoiqoLS2kYLLCqJ66y1NmzZ1AWttx+irmh7vh1h1iFWHSVQrXAF1bSv9X8FpBay1XfWcPheVpvnXi9/amxM22/ATT3bBV322+pFWT6Ba5sLmr4xgBcf/9re/uXUYPHiwnXTSSS6wLAoWaz9RzSMts2ojJbvttaTzZ7jHE7aa2YFmbjhor6z40g0aBwAAAKoj3UU5e/Zs15l2dN3a3Xff3Z0LKkmnsIzb4ujcUEkwsYNXU7Ysunbt6s7j1I+MzuOUmKTzU51bevRe3l2huhtWd40qkUnT66/uWNW5q6jEwUsvvWSffPKJuxNT89W5nc51dc6qc3nVzlWi1ldffeUSrrw7aZ966il3jnz//fdH3leD1j+RqHELIKmCR5puZc56a53exCpS9K0cOoDrQK4MS49uxVCGp6gejgKaCuTpCpx655TCShxoXgrqKvPUo4s4+mHRlT39Xz8UHv1QRD+OpWxWlRjQVUBdEY1eLmWL/utf/3LLPWDAABd4LEk9X/0wKfNVP2DR761l07IqCBxNz+lKpMpEKOisgO3555/v/npBZG0XXcmMbhBo+3h1hWK3uS5uKUPXo3WJ/jH2KBjsBXnV6aNuaVFh+ei6Qtq261YusN26D7ATTj7ZZfnqqqyW6ffff3cB48Lo/19//XW+Zdb27dChQ2R7qEyDR5m/sfWdko06AAQAAAAAJRkpQUV3XmrwKDlGAVt1xhXvXKkouiNRQyx1/qVz17IIBALuzk0lECmpRsutQKqSqzw6H9adrbpzUlm7CrCqwzSdrysB6emnn46ct3mBXQV/lXilDGPNX9tA20d3VirYq3KJSvRR0pDeV1RGT+eaqpEbTclB9913nyUKgdsEUzBBQQ86J0NNVtrg0dZgyYK85UlFzKMDjAoiegXLPV7AUBmeuv1CgVFljCo46h3MY6n0gTJtvbqv0bxO02Jvz1bwNh79oOpH58cff3RBRnUs5hVkV/BUVxIVVNbVQWWH6gcu9oclVtu2bV2gVPVqVRA+ejtoGQsrCeDdFqJArUoOHH/88e4WFO8HS+utWrC33HJLiba5gsDRZRSUBVvca7xAuXoC9YKr7r2DYftkVl3LydpqJ510sjVu1MhdPVVwWsFZlbgojJZZWdaxAePowvqxn1W8ovvJorY/f7F+AAAAANWXgpca4ontaMwT23eJ6K5GZegWReedidaiRYsi+2+JXUZlz+ou0Xh0d62Gwig5R3euFmb8+PFWGSiVkGCK2it4q0HjQE1U2uBR7cD/B+cqg4KAqqUTXW9HWayqLasC6Koh+9BDD7neM1VqQBdmpLD6mJqXSiXoap03L81bP4w6Jui2FN2SEp3NGl1zJ5aOJbpKqFtEtDxehqt6wNSt+wrierd/6McotnOteFSrRyUatF7Ry67bPrSc3rLrCuOoUaMidX/U6Zh+KNVRl25j8bJo9VrdXqJ6QN5rlSGs5SqMguLR21slH4pTv359l4Gr21O817Vq1coefPABW7divi36c6KtXrXKlWfQ+qkMgj6LeHVMtczKJI5eDpWiUNkHif2s9Dnqam0ya5HeyGr7S/Z98hm/UQAAAACQTAjcAkiq4JGma5HWyCqT6s2OGzfOBfxUw0a3S2hQrVYFRnV7hsoCKHD3/fffRzrr8oKZyohV3VfdeqHbNhTMvOaaa1yAUyUBVMdW89BtHwqu6jb+J554wmWDql5OdK+V8colqLMtvd8+++wTyURVxqsybDUfBRj1XsXVuPVovXQ7ierkelQz9oADDrCrr77apk+f7kpCjBw50tUqUtBUFJDWlVjdjuLVDRJ16KYgrzJuVWJAgWbdcqJAa2loW+qWnXh1g/RZ/fvf/3ZXerXNVfZhypRfrUnLDlarTkO3rMpA1mel4LLq2EYXm4+e/6mnnuo+CwWvNS8FbFV6onXr1m5a1b7VPqFguIr3q+O26BINyahVWmPrW3+PEk2b5k/u7GEAAAAAhScMIXmU9+eT3Gec1YCy5xS0UBYXnZOhpipN8Khfgz2tZSUHbnv06OGySl999VWX0araOA8++KArU6AAp+rhKHinAKpKA6jujcolqIdOUWkCBXSV5angrIKy+v4rSDtixAjXGZgCjKKsTv3/448/dqUZlD2q/xe3fI0aNXKdZEXfqq+Aowqvqx7P3//+d1d/tjQ1hVTaQOUEomk7KGtWAVJl8iorVcHMaNoOCtJGB25VW+iZZ55xAVCtl9b3tNNOcyUZSkP1i7QMjz76aKH/13pqfRUg1vso6P30089aRp0GttOuPewfF17kyj8okPzOO++46RRQ93oljZ6/AuwqPq/PTttBAWF1bKbXikpjKMtawXEFeffff/9IADtZ+X1+O6xJL+uQ3rLI6dpntLAMf/wSHQAAAAAqn86zdD6D5OV9PvqsyoMvXAND9crAUoBFnfAkumMZ1WDU7cy6jVm1KJO9HmKyC+fmWtZrr7nxjFNOMV8RtUCrIn0dddu96ntWt9Iaq3LW2zNLP7EFWdsDZvGCR+ftdKQ1r+TAbVVQnfeVssoNhu3FH/Lc+Bn9Uyw1wPZZlb3exq+aZL9snWPbompOK8NdF0sObdzTeu3S1WVcK4CtDOWaIieUZ08s2V4O48I2x9b4zOOqdmypyDZdZatJ61rd6HxA5YJ04TW6k04AfF+A0lLSjxKL1Jm2ys2p7aY2gtoGVaHtVhM88sgjrpaw7tKNFwMsTbuOKGKC6Yujjn/U+zlfonLg91uKd+t3kt+ijPwUjFVQ9ou1U+znjbNsayi70OARQVuUld9n1rWNPzIOs2ZpDW1wg342sFkvW5W7wXUAqFrSKkuiDHdl5tZUAZ/fetXfLTIOAAAAJCvdEfryyy/blVdeaVdccYUrb4fkoPJ76g9Hn4/uNi2vxE0Ctwmm+of6Iil7JdlrIVYFvkDAUvv0qezFwA5SUPbklgNsQOPutjJnPcEjJETA77O+u5LRFCscClvr9Ca2U0ZT9rzo/cXntwMadmWbAAAAIOmp1N7DDz9s1157rZ1yyinudnzdzZGamkqyYCXfXbNhwwaXAa1SeyqRWF4I3AKoUMrsU/BIAwAAAAAAKDn1ZaI+VyZNmuQ6TVZny7rTm2TByqPguTriVj8o6v+mPBG4TTBF29UpkQaNUy6h7NszvGWLG/fVqcP2BFDocWLz/ypx1E3fXrIGKOp3ZVNwqxuvF6A2GAAAAJKfMmz3228/69evH3XUqznu3U8wBWzVOdnEiRPdOMooL8+y33nHDRoHgAKHiZDZmz/nuUHjQFFyw0H7z7LP3KBxAAAAAEgWBG4BAAAAAAAAIMlQKiHBVGOkf//+dE4GAAAAAAAAoMTIuE0w1VZMSUlxA3UWAQAAaoacnBw75phj7Oeff447ze+//27Dhw+37t272/HHH28zZ86s0GUEAABAciNwCwAAAJSj7Oxsu/LKK10vz/Fs3brVzj//fNcz9DvvvGM9e/a0Cy64wD0PAAAACIHbBFOHZAsWLLAlS5bQORlQSQ455BDbY489Ch2KyoQqyqOPPmqnn366G9cJt94jmeiYo/XT3x2xdu1a++STT8pteTZv3mzvvfdeuc2vvOcf/XkCQFnMnTvXTjzxRFu0aFGR040bN87S09Pt2muvtV133dVuvPFGq1Onjo0fP54PAAAAAA41bhMsHA67wK0yL/bcc89Evx2Q9ELhsK3aGLY1G8O2LdesVqpZ0/o+a17fZ36fL2Hve8MNN9hRRx1V4PkGDRqUed6a74ABA6w6eeCBB9zx68gjjyyX+T3//PMuSD5kyJBymV9Fzx8ASmrixInWr18/u+KKK6xHjx5xp5s2bZr17t07UkpLf3v16mVTp061YcOGscEBAABA4DbR1Ahv3bq1u+2NGrflwO+3lD32iIyjalmzKWQ/zA7ZlIVBF7T1KHjbs13A+u/ht6b1EvO51qtXz5o1a5aQeWdkZLihOlHQNpnnV9T8FQPp1Hr7fpTAawGoJnTBqFvdDpFxoKxOPfXUEk23evVq22233fI916RJkyLLK0gwGHRDLLUz1Slu9HRFCQQCNWJa3f1W1G9QaabV9vXa86WdVkO85S7LfKvStJpO08cTvQ8zbem3Q3HfjapyjNBz3velJh0jmJbv/Y4e/6K/M0VNW12OEdGq+jGipMi4TTB9MB07drTMzMx8XwLsGF8gYKn9+rH5qmjQ9tUJebZkXcEDmYK4P84N2qJ1ITt135SEBW+LsnLlSrv77rttwoQJtm3bNtt9993tpptuctlQKjcwcOBAu/TSS11m57HHHmuNGjWKvFalEkaPHm1fffWVe/znn3/anXfe6bKpWrVqZWeccYaddtppkVvy//jjD3dM0Mm5Xte3b998ma7Ktnr55Zcjz/3rX/+y6dOnu/fWrbUPP/ywLVu2zF0Uuvrqq+2www7boU5zHnzwQTe/devWWYsWLVxtxZNOOskt47vvvhvJHNN6bdy40a3Tl19+abVr17bDDz/crrnmGhewVqbryJEj7YADDrCPPvrIzUd1G2O3j6h8w+zZs11pCWXzvv/++9a0aVP3ftoe8babfgCfeuope/PNN23VqlXWsGFDO/nkk+2SSy4pdP592gVt1KhRdsWHH7rntWz6PPU671bmm2++2XUMpE6BYoMnqDlSfAE7uHH8rEggUfRbk5aWlu85PdbxuSiTJ08utLHfuHFj69q1a+TxDz/8EPfERMdCHfs8P/74o+XmRl1RjbnwqUxgj475WVlZhU6rUg+q2ev55ZdfbMuWLYVOq98PZSZ7fv31V9u0aVOh06amptp+++0XeazfiQ0bNsQ9gerfv3/k8YwZM9zvXDwHHXRQZFy/CQqox6P5eido+q1ZsWJF3Gn33XffyOer3zcN+i0tLJFD28G7APzXX38VWeZI21fbWXRX38KFC+NOq7rJ9evXd+OLFy+2efPmxZ1W+4P3G7l06VL3OxlPly5d3EUG0TbQtohnr732ilw417bVNo5Hv+EtW7aMlGwqqrM+/W7vtNNOblz7gvaJeHbZZRdr27atG9dnMGXKlLjTtmvXztq3b+/Gte9qH46nTZs2rsyJ6DtRVPkttdnUthR9x9XejEfbQNtC9B3WdzkebVttY8+3334bd9qqcoxQm0/tct2xqu9RTTlGqG0fD8eI7ThGFH6M+P777913prDfmOp4jKgu7Qj9hpcUgVsAFVIeQZm2hQVto+n/P/wZsuN6JbZsQmEUANXJzeuvv+4ajAqg3nbbbfbh/wJ/3o/B22+/7U6Yo5+Pph+h8847z4YOHeqCkDpJUoBQP0LebfwKfmreuoW2Q4ftmX6eo48+2p577jl3wuKdFH366ad27rnnuudUC/GOO+5wwV7VdL3qqqvsu+++i5xsldTTTz9t33zzjQvS6n0UONXyKkB9zjnnuBNHueWWW9xf1V7Uj/Frr73mGtJ33XWXW4577rkncpKnExEFUfXDGFtKQg1SnSjp/TzahlpXbW/Ns6jtpnV94YUXXBBbJ19qoGgbHnzwwYXOX9PphO+ZZ55xNSQfeughu+yyy9w8tJxeh0Baj59++smtR3SDAgASTcem2CCtHhd3B4d+C3TMjKWTlegTrOXLl8fN5tDJXXRGiAIG8U7OdGITnXyg4328kzOtU0rK/59eKABZ2LKKfit0odKjwGK8Ezmd5MROG68TNy1r9LRahngnfRI9rbZDvJM+70TP2xYKyOgibDy64OptC51QR/+NpZNC77dTJ3xFnSDqt03bWdasWeP2h3h+++03q1WrlhvXPIs6mdT768KsrF+/3l0kjUefR926dd24tkFRwSltL+0zom1b1LTi/V+164uaVvu2tz7aF4qaVvu2t510waSoabVveyfz2neLmlbfV29/0euKmlb7trcf5uXlFTmtllGDt55FTat5Rh9Hipq2qh0jtHw16RgRL4gkHCP+H8eIgseIon5jqvMxIrWKtyP0G64EppIgcIsqxR0cvC9jRgblJ6oI1bRVeYSSmLIgaPvu5rcWDco3cHvrrbe6gGBs9sPHH3/s9qtDDz3UZZF6mR7K9IzOGpUzzzzTdt555yLfR8FIBUIvv/xy91hZG/pxevHFFyOBWx2gTznllEJf36lTJ/eaL774wmW/6mq9Xq+sWv0Q6EdOy6gsEy2jrnR6J3CloZrb++yzT6T+4j/+8Q977LHH3JU/BTS9wIGuvKqDHS2Psm91xVS0LbU+yrT1KLisTJVYmpdOBvXjGl2u4rjjjotcLR47dmyR200ZuPfee28k80LbT8urgG3nzp3zzV8/wspYfvX1t6xr5z3ccULZt7oiq+2pRogaEAr86nXKltG6FXWijOpL3/9toe0N2lr+NH5XUGF0p4Ma7dH0uHnz5kW+Tr9XXjCuqFsco7Nmiru1rzpP261btxLf4ljctDt6a7N+uxV4UaZq9PuVdb5VbdpElj+IzroqbtroO52KmzY686u4aYu6ABw7rbKhSzptdFZbUdMW992oKscIBX90gULfl+i7EqrzMYJpOUaU5Rih74r3nYn9jamOx4jq0o7QBcqi7m6JRuA2wbzUdV0lUCZb9NUD7IC8PMsaO9aNZijwFZPZh+TkdURWEppu9SazFmXvMywflTkYNGhQvue876MOngoEqmyAsmrnz5/vfvxiTxi8W/KKokzRWbNm5fuxja3RVdx8lEH62WefucCt/uq2DmXUqiM1dYJ29tlnu0xd3Wqh4G1hJ/DF0Yn/f//7X7vvvvvcMnu3LhZ2pVTZt9oWBx54YL7n9Vz0LZq6ZbA0ordDcdtNQWZdoVR5By2Pyk0o06awk7oFixa7k+RTTz3ZUvy+SJ1bTavAtK6wKjDsZRZ5P95F3TqE6is3HLRnlo5z4xe2OdbSfPxOo2IoGKO7AtTA1++Q/uo3SBfSiqKLVLElFgpTWICQaStnO3gna/rcintddf7ckHjJ8DmXdVq1/wr7viTr8jJt+W0H7Bgv4FrTf2MCVWza2LtUi8LZSQVQQzzRHfMAyaykQVtPVm75f1+UzVlYNqgX0FN5AN3yoaCp6q8q8Kf6qdFKktmq29+UFeqVGChMcfPRMqieq5ZHgdu///3vkR9lPa9bq1RuYfz48a50w6uvvuoydUtDpQOU5aqey5XRqoxkrXdh1IBWpq3eq7CMMa+uXGkzf6OnL267aVlVzmD48OEuAH/ddde5GriFLm/e9uDzqde+ZCftV89SAr58+4FXDmNHfzgBYEfpgpOOp7oT4YgjjnAXo1RfXTW7dWzSrY+q/w0AAAAIvWUlmK58KBihLDI6J0NNVauUMbGM1Iqtb6tbFCZNmuQ6/1Kmk7Javdpupb3ookxYZewq+1SBYg2qe/PSSy+VeB66dV+DTuKVIarsWFGm6f333+9uwVBJAf1fJQSU1V9aeq1qyKq2rwLFXp0kb32jC9trnVSrR89566T6QSo/UFwnOp7COmMpzXZTbd2LL77YbrjhBhdoVudwqldX2PK23bmt+fwBy9q8wXb+37xUi0+lFvQadQ6i7Rpdf0gZvACQaLpTQnd3iI5LuhinzsZ0EU0XwVR/PPpuAAAAANRslRq4VfkAnYSrdpAasmPGjCn2NSoMrCBobK+dCrio13D9T/P0ghCVTcEEZZVpKC5wAVRXTev7Shy81XTNtpdRLVcK0inTKXZQPVR1SqYLK6p3q7qqymT1OrkqaWAyum6rgprKHFWgVbffK5vK62ispNRJ2RNPPOHKE3gdgGg5FcB8/PHH3bFQpQ60vNE9hcZSQFqdl0UPCnaq9MLXX3/tygaot051eha9viq/oHmryL2CyDq+KsirbF91dqLatt62KwnNT8HweD1lF7fdFKhVD8xeGYsrrrjCZUVHL683/zp16lr3A463z1650yZO/NkF5rV+KuugwLBKTyjgrQ7X9F7qUM0LpABAeVJd7ejam3qsIK1HF+LUOaSOrbqzoKjjOQAAAGqeSg3cKltLJ+Dq5Vu36Y4ePdoFTIqizmRie39Tj+t6rXo417yUsfDPf/4zwUsPoKSa1/dZz3Ylq/fSs33AmtUv/4scus1eF4hiB130UWdfOrao1uAxxxzjMp5uuukmVwPXq/1aUgqyaj7K6FRmqOajOrQXXHBBqeajLFgd6xTA9ajjLQWUdczT8zrOXXnllW494rn++uvtvPPOyzeo9IG2h7JMNR8FYXXLrgIIXubp4MGDXZBUAVUFenW8VtDzrLPOitTY/de//lXi9VHnaipJofcrrAfs4rabLsipgLuWa8SIEa5TM83TW97Y+R98wjXWrtM+dsVll9qJJ57oPkt9rqo7pLIIynJTT79Dhw51wXC9FwAAAAAAycQXrqTiqwpIqLMZnah7mQjKIlNGVbxbij/44AN3gq2OG9TTuPc6nXBrXjqZF2WPqSbkTz/9VGinPXpvneyrJmSib0dTIEEZbVu2bHGBBopzl004N9eyXnst0jmZr5rVpdTXUcEkdUJV3TK012wK2asT8mzJuviHnDaNfXbqvinWtB5VXGryvlJWucGwvfhDnhs/o3+KpUbVuK2pSrK/KDCvLGt1GhcvM7o6ygnl2RNLPvz/zsn8Nbv8f1U7tlRkm66y1aR1rW50wVTlf3r06MG5AMD3BeA3Blaadl2lRUfUe7g6o4nuQbx3794uW7awXsLXr1/vssuUVRvbEJoxY4Yrt+BRo0i30Oo9kuEESL2lL1q0iA7KUKMpGKug7H67BwqUTdBjPU/QFgAAAAAAYLtKSytRbUnVLExLS4s817RpU1f3dsOGDda4ceN80993333ullZ1KhNNva7rNc2bN488p1tiVb9xxYoVxQZVKyLhWL2ue+UdKinBudoI+3wW2GWXyLhVs+3p7ZPVdT9pUtdnx/b02z67+W3NprBl5ZhlpCmo63N1bf0+X7Vd9/JW3feVsgnbbi28TEFto0penCq4v9Sk/Up7Sqc6bSPjNWndq8OxpaosJwAAAFClArfqPCw6aCve49jOgH788UfX4+5HH31UYD7qzCb6tdHzKq5TIdVLVGZuoqkTHAVuvV7ZUUZdumz/u3lztduUOgH1gvzVeV/J0G3Z2/vb2i5stmljJS5QFVRT9pUd1b3l9r9bNlX2klSd/cULgHm3ytckfQO7ub9bNla/35XqfmzRxXsAAACguqq0wG16enqBwKr3OCNDYZ3tvF7G1XlZ9PPR84l+bfS8CqtvG9sZTkXUCPNOhqtKvThUHvYVsK+gso4t3vP6q+lQM1W136HYDmsBAACA6qTSArcqH6C6tapzq9IGXvkEBWfr168fmW769Omuc69LL7003+vVM7p6HldP8ArerlmzxnbddVf3P81T5RbUA3tRdEJSUScl3ntVhZOgpD+hzNve6ZClpFTL7cm+AvaVsh8n8v5XKj3FXzWyBpPt2FKTtpn2l9xw0I2n+gI1at2rw+9QVVhGAAAAoMoFbtVzmgK26mHV61hM5RC6du1qfv//95nWrVs3++yzz/K9dtCgQXbXXXfZ/vvv76bVa/Tafv36uf9rnpr3nnvuaZVNnaep1IMyhw8++OBIkBo7KC/Psl57zY1mnHKKWWpML1cAajwFbV/8YfsFnjP6p1hqoMZvEhRBQdsnlnzoxi9sc6yl+fidBgAAAJAcKu3sRGUMvIzZe+65x1atWmVjxoyxe++9N5J9W69ePZeB265du0Izdps0aeLGTz31VFdOoWPHjq6TMs3zxBNPLLZUQkVRBrACuAAAAAAAAABQEpWaVjJy5EgXZD3zzDNdvdkRI0a4bFrp37+/C+IOGzas2PkcffTRtnTpUhe8VW1bzeOaa66xZKCM4L59+9rGjRvzZRIDAAAAAAAAQFIGbpURe//997sh1uzZs+O+rrD/nX/++W5Ixtpr6gAtNzeXOmwAAAAAAAAASoQUUADV3h577GE///zzDr128+bN9t5771myuf76611db9Xy1vpFD48++ugOzVPbSK+XJUuWuHH9LU9XXXWVq/stX375pR144IHWvXt3+/7770s9r9NPP73IdR11fhebOPHnYte1MJqv5l8err76avvvf/9bLvMCAAAAANQc9MCRYKFQyJYtW+aCP6rZGwjQSw5qtnAoZKHVqy20dq1ZVpZZRob5mzQxf7Nm5kvCciLPP/+8C/KpJneyOfTQQ+3WW28tkM2vLP+yatWqlf3www/WuHFjKy8//fSTrVy50vbbbz/3+JFHHnFlcS6++OJIzfLydNE/v7GePcpv+XeUygBdeOGF7gJAWlpaZS8OAAAAAKCKIHCbYOFw2ObMmWPZ2dm22267JfrtgKSmYG3ujz9a7vTp24O2nowMS+3WzVL3288FcZPtO5ys0tPTrZkC3jGB2/Kgi0yad3l6/PHH7ZRTTok83rRpk/Xu3dt22mknS4S6DZpaalrl/8ypg83WrVvbuHHjkvICAAAAAAAgOSVfels1o4CKgh/KWktEcKXG8fst0K6dGzSOqhW0zR471nInTswftJWsLPe8/u8ycSs4MPvkk0/aIYccYl26dHEZoKNHj3b/e+edd9z4xIkTI7fVqwPEu+66y5Up0KDb4Dds2JCvvMBjjz1me++9t91xxx353uuvv/5y/1+8eHHkuQULFrhyB8uXL3fZ+eecc4717NnT9t13X7vzzjtdfewdpWVVJ48HHHCAde7c2a3jG2+8Efm/Hv/zn/9066yAYnSQOrZUgjpYVKePvXr1ctNr2bL+9zkqI1nzUvavArFPP/10gWWZN2+e/frrr3bQQQdF3ludSt5www1uXFasWGGXXXaZ69BR21bbWevgfRYnn3yyy87Ve3zwwQeR1/ztb3+zrl272oknnmizZs0yHWrbN/W5UgmTJm0vlaC7Hq688kq3bQ8//HCbMWNGvuWbO3euCyqrbMMZZ5xh69evz/f/X375xXWW2a1bNzv22GPt008/zVe2Qtv58ssvd6/XOsaW19A6vv766zv4SSKR/D6f7Va7tRs0DgAAAADJgshXojew3+8CJh07dnTjKBtfIGBpBx3kBo2j6pRHUKZtcOnSIqfT/3MnTHDTVxQF2F544QW7++67bfz48S4wqPqmv/32mx111FGRQKrKBsi//vUvmzlzpj3zzDP24osvuoCggo3RFKB8++23XQAw2q677uqCtJ9//nnkOQUANX+VJlAwVGUOtEwK/up/b7755g6vmwKo33zzjVsfrZuCs3qPNWvWRKb58MMP7bnnnrP77ruvyItLN954o8uQfe2111zmrAKf0YFpBWEVZFWA9ZhjjinwetWwVdCzbt267vFbb71lLVu2dIFbjeu1Z555pm3bts1eeukl+/e//+2WfdSoUZF5TJkyxd25oG2i4LG8++67dsQRR7ht1rZtW7vkkkvMFw7ZwM7bM20D/1snBZUVPH755Zftpptusv/85z+R+eq91bmlXq/lV2A3OsC9evVqu+CCC1zgVtvr3HPPdcFaBXM9r7zyijvWf/TRRzZo0CD3ftpenv3339+mTZvmAuBILim+gB3dtJ8bNA4AAAAAyYJIIoCEU01bVx6hBHKnTbNQVGAx0RQwVbakMlzbtGnjsi6VJa8SJxkZGS6Qmpqa6p5TUFGBv9tvv90FIZWRqsCiMnJnz54dmacCkDvvvLO1b9++wPsdffTR9tlnn0UeKzirALEX/FQtbN1Wr8xWBV69DNXCKBir6RT4jR6UuSsKEisg3aNHDxeU/Mc//uEyeJXl6znuuOPcemjaeBYtWmRffPGFy87VtFp3BYAVNI0OTiqg6ZUFiPX777+7wLVHdyGoHIPWV+MK7Kr+rfce+jxuueUWFyjesmWLe40Cy6oVq/l4tXdV51cZt3pOn8vatWsLdASmZfzkk09cwFbBVWUgX3TRRZH/q7M0ZU3fdtttbj6nnXaam290UFZ1efU+Wr/BgwfbSSed5AL+Hi3zeeed57azAvnKRtY+5NHzKSkp9scff8TdzgAAAAAARKv84n8Aqr1IR2QlkZVlYQVumze3irDPPvu4TMgHH3zQlTJQYE0ZlupYMJZKHCjwqVv2o2laBUMVFJSiarYqSPvQQw+5IKXmpVv7lTHqBT6VgaqM3AMPPNBNu9dee8WdlwKQI0eOLJAp2/x/207BRwUxlU2rbFMFTyUYDEamLUl9WW0XraOWKXa9Fy5cGHmswHc869ats06dOhX5Hgp0N2jQIPKcgtJ5eXkucCzqwEzB9GgKInuUzduhQwe3rtHLOn/+fLfO0cFplVaILpOg947u1E3///bbb9245vf111+7oLhHn53eyxMdpPeyirXsHt1xoXVTYBkAAAAAgJIgcJtgChao/qOyrxRIUMYVdlw4N9eyXnvNjWeccor5UlPZnFVBSYO2/xPOzraKMnbsWLvnnnts+PDh7hb36667rkCJA48X8Hz11VfzBfm8oKJX61adhsWj4KaCgspgVaeFffr0iXQCpuxXZZrqfyoTcOmll7osziuuuKLQedWpU8dlgMYrcaAAsdZPt/irTIJu3/fqyXqKWtbo9VZmrMo/xGrRooULfBc3Ly1jdMA4VmGv9ab3/hY2jbJ2Y4PJ/kCKPfft9trAeaHCO5dLS0srshM6ZVl7FIBVXVtlLEeLPp5HTx9vnm7ZKJmTdHJCefbEkg/d+IVtjrU0P7/TAAAAAJIDpRIqgIIzXgc7QI0UkyVZHF8JgonlRbfiq66tMl0V3GzUqJHLivSCbtFBUd3urkChArQKmGpQdqVKLZQmk1KZtArMKkCr0gnRgVbNR+UannrqKdfZVXRZhdJSZ1g333yz60BN76lSD4UFFIujzFKVG9C28NZbF6NUJqKkx7bowHa891DWcvQ0U6dOdcFRlZ2I588//4yMq36s5tGhwy75ptlll11cYDW6QzIv+1h2331397rosg/RJQ20bMos9tZdw5dffunq3ZaUgraZmZnWtGnTEr8GAAAAAFCzEbhN9Ab2+11GnTLsyLRCTeVv0qTkwduMDPMlILg1ffp0++677/INCmQqUDthwgR3O706HVN2q26D9wKStWrVslWrVtmSJUtckFaZuaqFqkx63WJ/7bXXuqBeUWUCYh155JGuYyu9n7J8PbolXx1+qXyC6qPqVv2iSiXoopDKOsQOChBKw4YN3S3+KvGg99OySmkvJKnuq8oyKACs7aiO21SiYevWrVa/fv0SzUPrEV0HOJY671JgXMuo6X766SdXR1cdnRX1HgqeqrMyfRYKviuo2m+fffJNo89NdWk1P2UH67MbPXp05P+qX6tax+qATSUb1EHZuHHjIv8/9dRT3WelwLoCvHpPdVJXWC3feDRfKaqWMAAAAAAA0QjcJpgy1BQ00C3NRfXYDlRn/mbNLDWqFmlRUrt3N38CArcPPPCAKzsQPSggq2Df5s2bXWBvxIgRrpOpww47LJJxqXFlSyozVtmw119/vStnoDIGJ554ossIVSdisbfsF0XlBbp06WJ9+/Z1gWOPAsLKyDz99NPdvFWrVsHEeJSxq4Bq//798w3qHEtUAkLroWVXoFW1dFUTdkc6yFJ2rYLTZ511lp199tkuC1XBy5LSciog7XU0Fkvb7/HHH3fjWvcrr7zSBg4c6ALZRdG2euutt2zo0KEu41YB2cKOtco8Vo1aLbs+Q3U05lE2rjKcFfDWfJSFrQ7KousAP/nkk64DNQWS//3vf7t5qLRFSU2ePNm9v1f/FgAAAACA4vjCpb1nthpQlpgCF+ooJ7ZOZSJoEysgoI5pCN6WcVtW8xq31XlfUQdl2WPHWnDp0rjTBHbaydKHD9+eoYtqt68oyHr88ce7khSJlBsM24s/bO8Y7Iz+KZYa8CXFup9wwgnuAkGy7i8KzC9dutQFqpVhXlNQ47ZqH1squk1XmWrSulY3qtWu8j89evQo1YVeoCbi+wLwnakJtpaiXUfGbYIpU2/FihVxe6kHagoFYxWUTe3bt2DZhIwMS+3Xj6BtNXfBBRe4urs1jcokLF++3NUZBgAAAACgpOg6uQIyV3R7sGpR6tZioKYHb9OOPtpS+vWz8Jo1Fs7Odh2RqaatyiP4/FxLqs5UxkFlDX744Qc3XlM89thjdsstt7iSDAAAAAAAlBSB2wTTbYbqTV1p0FXhlsOk5/e72+m9cVQ9Cs4Gmjc304AaR/VhE02H2raNtx9vk+GwW5pawKh4fp/P2tdqERkHAAAAgGRB4DbB/H6/de3a1dWL0zjKxhcIWNrAgWxGAHGl+H02qCs/byiZFF/ABjfbj80FAAAAIOkQSQQAAAAAAACAJEPgFgAAAAAAAACSDPeSJlgwGLRJkyZZVlaW64wnJYVNXhbh3FzLfvNNN55+4onmo7MfADFyg2F79cc8N37qfimWGqBuKeLLCeXZM8vGufHzWh9laX5+pwEAAAAkB85OKsC2bdssOzu7It6qRggHg5W9CACSXF6ospcAVUleiN8VAAAAAMmHwG2CqUOynj172saNG+mcDAAAAAAAAECJUOM2wXw+nzVo0MDq16/vxgFUrOuvv9722GOPuMOzzz7r/i5btizf6w4//HDr2rVrvmz53Nxc69atm33xxRf2888/x53n2LFj3fSHHHJI3Gn0+vKwcOFC+/vf/+4uEA0YMMCtT1F+//13Gz58uHXv3t2OP/54mzlzZr7/jx8/3q17jx497JxzzrGlS5dG/rd8+XK74IILrFevXm7dnn/++Xyv/eabb2zw4MFuWY499lj78ssvy2UdAQAAAACoici4BVCt3XjjjXbVVVe58XHjxtmYMWPsrbfeivxfF1Yee+wxmz59urVu3do9t3LlShewrFu3rk2ZMsX22Wcf9/wff/xhOTk51qdPH5s9e7Z77ocffijwnvXq1YuM33DDDXbUUUcVmEbvW1ahUMgFUhVgfvfdd10Q98orr7QWLVq4wGmsrVu32vnnn+/+d99999lrr73mXv/5559b7dq17ddff3Xb6uabb7a+ffvaqFGj3PzeeOMN9/rLL7/cbaN33nnH5s6da1dffbXttNNOdthhh9msWbPskksusWuvvdYOOuggt10uu+wyt6333HPPMq8rAAAAAAA1DRm3CRYOh23VqlW2du1aNw6gYimI2qxZMzdoPBAIRB5rSEtLc9mlCtx6lA3bpUsXF7yMzoydOnWqdezY0Ro2bBh5Lnpe3pCRkVHo+8e+b1mtW7fOOnXqZLfddpu1b9/eBUz33Xdfmzx5cqHTK3Cdnp7ugqu77rqrC2rXqVPHZdmKgtrHHXecnXzyybbLLru4/69evdq9T2Zmplv/Cy+80L3XoYceagcccIBNmDDBvfajjz5yAe4zzjjD2rVrZ6eddpr169fPPvnkkzKvJwAAAAAANRGB2wRTRpxuTZ4zZ44bB5B8lEE7Y8aMyGMFaxW01fDTTz9Fnp82bZp7LlGWLFkSt7SCslxjNW3a1B566CGXGawLQwrYTpo0Ke4yavl79+4dKduivyp7oICsTJw40WXPetq2bWtfffWVNW7c2AWja9Wq5ZZDJSPmzZvnMnQVOJahQ4e6DNxYmzZtKrftAwAAAABATUKphAqg7Lxt27ZVxFtVfz6f+Vu0iIwD5RW4fe6559zFFXUoqMCtslibN2/uSgqoxIBKCahsgmrmJkqrVq0KLb0QW36hMKo5qzq9Bx98sKtRWxhlz+622275nmvSpIm7sKQOFJVVGwwGXc1clT5QPV9tB5VeUKbuLbfcYnfeeae9+OKLbrphw4a5ermiDN5omqeycZW9W9F0ZGjV8H/B6Qp/d1Q12kd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" ] @@ -536,10 +536,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.428919Z", - "iopub.status.busy": "2026-08-10T22:40:30.428842Z", - "iopub.status.idle": "2026-08-10T22:40:30.436012Z", - "shell.execute_reply": "2026-08-10T22:40:30.435670Z" + "iopub.execute_input": "2026-08-18T20:30:20.376501Z", + "iopub.status.busy": "2026-08-18T20:30:20.376427Z", + "iopub.status.idle": "2026-08-18T20:30:20.384014Z", + "shell.execute_reply": "2026-08-18T20:30:20.383718Z" } }, "outputs": [ @@ -638,10 +638,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.436919Z", - "iopub.status.busy": "2026-08-10T22:40:30.436838Z", - "iopub.status.idle": "2026-08-10T22:40:30.438652Z", - "shell.execute_reply": "2026-08-10T22:40:30.438331Z" + "iopub.execute_input": "2026-08-18T20:30:20.385079Z", + "iopub.status.busy": "2026-08-18T20:30:20.385005Z", + "iopub.status.idle": "2026-08-18T20:30:20.387087Z", + "shell.execute_reply": "2026-08-18T20:30:20.386699Z" } }, "outputs": [ @@ -684,10 +684,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.439445Z", - "iopub.status.busy": "2026-08-10T22:40:30.439387Z", - "iopub.status.idle": "2026-08-10T22:40:30.443430Z", - "shell.execute_reply": "2026-08-10T22:40:30.443201Z" + "iopub.execute_input": "2026-08-18T20:30:20.387926Z", + "iopub.status.busy": "2026-08-18T20:30:20.387873Z", + "iopub.status.idle": "2026-08-18T20:30:20.392017Z", + "shell.execute_reply": "2026-08-18T20:30:20.391665Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.444432Z", - "iopub.status.busy": "2026-08-10T22:40:30.444378Z", - "iopub.status.idle": "2026-08-10T22:40:30.446169Z", - "shell.execute_reply": "2026-08-10T22:40:30.445905Z" + "iopub.execute_input": "2026-08-18T20:30:20.392856Z", + "iopub.status.busy": "2026-08-18T20:30:20.392810Z", + "iopub.status.idle": "2026-08-18T20:30:20.394336Z", + "shell.execute_reply": "2026-08-18T20:30:20.394045Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.446985Z", - "iopub.status.busy": "2026-08-10T22:40:30.446925Z", - "iopub.status.idle": "2026-08-10T22:40:30.448880Z", - "shell.execute_reply": "2026-08-10T22:40:30.448628Z" + "iopub.execute_input": "2026-08-18T20:30:20.395194Z", + "iopub.status.busy": "2026-08-18T20:30:20.395123Z", + "iopub.status.idle": "2026-08-18T20:30:20.397174Z", + "shell.execute_reply": "2026-08-18T20:30:20.396862Z" } }, "outputs": [ @@ -1027,10 +1027,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.449746Z", - "iopub.status.busy": "2026-08-10T22:40:30.449692Z", - "iopub.status.idle": "2026-08-10T22:40:30.452362Z", - "shell.execute_reply": "2026-08-10T22:40:30.452014Z" + "iopub.execute_input": "2026-08-18T20:30:20.398024Z", + "iopub.status.busy": "2026-08-18T20:30:20.397974Z", + "iopub.status.idle": "2026-08-18T20:30:20.400638Z", + "shell.execute_reply": "2026-08-18T20:30:20.400293Z" } }, "outputs": [ @@ -1086,12 +1086,7 @@ "- `'mammen'` - Two-point distribution, matches first 3 moments\n", "- `'webb'` - Six-point distribution, recommended for very few clusters (<10)\n", "\n", - "**Note (3.9):** on a *bootstrapped* fit, fit-time `aggregate=` remains the\n", - "documented route until 4.0 - post-fit `results.aggregate('event_study')`\n", - "raises there because the percentile draws are not retained\n", - "(`aggregate('simple')` and, where supported, `aggregate('total')` relay\n", - "the stored bootstrap inference). The\n", - "`FutureWarning` in the next cell's output is expected." + "**Note:** post-fit aggregation works on *bootstrapped* fits too — `results.aggregate('event_study')` replays the fit-time multiplier bootstrap from the retained RNG state, reproducing the percentile inference without a refit (`aggregate('simple')` relays the stored overall inference bit-exactly).\n" ] }, { @@ -1099,10 +1094,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.453142Z", - "iopub.status.busy": "2026-08-10T22:40:30.453091Z", - "iopub.status.idle": "2026-08-10T22:40:30.460107Z", - "shell.execute_reply": "2026-08-10T22:40:30.459862Z" + "iopub.execute_input": "2026-08-18T20:30:20.401508Z", + "iopub.status.busy": "2026-08-18T20:30:20.401460Z", + "iopub.status.idle": "2026-08-18T20:30:20.407541Z", + "shell.execute_reply": "2026-08-18T20:30:20.407283Z" } }, "outputs": [ @@ -1110,29 +1105,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bootstrap Inference Results:" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "Bootstrap Inference Results:\n", "============================================================\n", "\n", "Overall ATT: 3.4763\n", - "Bootstrap SE: 0.1173\n", - "Bootstrap 95% CI: [3.2434, 3.6959]\n", + "Bootstrap SE: 0.1128\n", + "Bootstrap 95% CI: [3.2577, 3.6826]\n", "Bootstrap p-value: 0.0020\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ".py:9: FutureWarning: CallawaySantAnna.fit(aggregate=) is deprecated and will be removed in 4.0. Fit once, then aggregate as a post-fit step: results = CallawaySantAnna().fit(...); results.aggregate('event_study') / .aggregate('group') / .aggregate('simple') / .aggregate('total'). balance_e moves onto aggregate() alongside it: results.aggregate('event_study', balance_e=2).\n", - " results_boot = cs_boot.fit(\n" - ] } ], "source": [ @@ -1150,12 +1130,6 @@ " unit=\"unit\",\n", " time=\"period\",\n", " first_treat=\"first_treat\", # Column with first treatment period\n", - " # Fit-time aggregation is the documented exception for BOOTSTRAPPED fits\n", - " # until 4.0: post-fit results.aggregate('event_study') raises here because\n", - " # the percentile draws are not retained (aggregate('simple') and, where\n", - " # supported, aggregate('total') relay the stored bootstrap inference).\n", - " # Expect a FutureWarning.\n", - " aggregate=\"event_study\"\n", ")\n", "\n", "# Access bootstrap results\n", @@ -1173,10 +1147,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.461015Z", - "iopub.status.busy": "2026-08-10T22:40:30.460957Z", - "iopub.status.idle": "2026-08-10T22:40:30.463002Z", - "shell.execute_reply": "2026-08-10T22:40:30.462737Z" + "iopub.execute_input": "2026-08-18T20:30:20.408539Z", + "iopub.status.busy": "2026-08-18T20:30:20.408491Z", + "iopub.status.idle": "2026-08-18T20:30:20.412698Z", + "shell.execute_reply": "2026-08-18T20:30:20.412358Z" } }, "outputs": [ @@ -1188,35 +1162,37 @@ "Event Study with Bootstrap Inference:\n", " Event Time ATT Boot SE Boot 95% CI p-value\n", "----------------------------------------------------------------------\n", - " -4 -0.0522 0.1360 [ -0.3051, 0.2115] 0.6814 \n", - " -3 0.1839 0.1612 [ -0.1242, 0.5219] 0.2565 \n", - " -2 0.0637 0.1172 [ -0.1633, 0.2890] 0.5812 \n", - " -1 0.1720 0.1245 [ -0.0728, 0.4159] 0.1643 \n", - " 0 1.8625 0.1056 [ 1.6533, 2.0716] 0.0020 *\n", - " 1 2.8985 0.1053 [ 2.7072, 3.1061] 0.0020 *\n", - " 2 4.0353 0.1121 [ 3.8413, 4.2733] 0.0020 *\n", - " 3 4.7591 0.1884 [ 4.4022, 5.1135] 0.0020 *\n", - " 4 5.8218 0.1724 [ 5.4790, 6.1182] 0.0020 *\n" + " -4 -0.0522 0.1383 [ -0.3218, 0.1937] 0.7214 \n", + " -3 0.1839 0.1594 [ -0.1466, 0.4897] 0.2565 \n", + " -2 0.0637 0.1126 [ -0.1598, 0.2774] 0.5972 \n", + " -1 0.1720 0.1194 [ -0.0422, 0.4024] 0.1283 \n", + " 0 1.8625 0.1086 [ 1.6574, 2.0823] 0.0020 *\n", + " 1 2.8985 0.1003 [ 2.7205, 3.1006] 0.0020 *\n", + " 2 4.0353 0.1134 [ 3.8120, 4.2634] 0.0020 *\n", + " 3 4.7591 0.1714 [ 4.4127, 5.0785] 0.0020 *\n", + " 4 5.8218 0.1673 [ 5.4647, 6.1565] 0.0020 *\n" ] } ], "source": [ - "# Event study with bootstrap confidence intervals\n", + "# Event study with bootstrap confidence intervals: post-fit aggregation\n", + "# replays the fit-time multiplier bootstrap (percentile inference), so no\n", + "# refit and no deprecated fit-time aggregate= are needed.\n", + "es_boot = results_boot.aggregate('event_study')\n", + "\n", "print(\"\\nEvent Study with Bootstrap Inference:\")\n", "print(f\"{'Event Time':>12} {'ATT':>10} {'Boot SE':>10} {'Boot 95% CI':>25} {'p-value':>10}\")\n", "print(\"-\" * 70)\n", "\n", - "event_ses = results_boot.bootstrap_results.event_study_ses\n", - "event_cis = results_boot.bootstrap_results.event_study_cis\n", - "event_pvals = results_boot.bootstrap_results.event_study_p_values\n", - "\n", - "for event_time in sorted(event_ses.keys()):\n", - " att = results_boot.event_study_effects[event_time]['effect']\n", - " se = event_ses[event_time]\n", - " ci = event_cis[event_time]\n", - " pval = event_pvals[event_time]\n", + "for i, event_time in enumerate(es_boot.event_time):\n", + " if bool(es_boot.is_reference[i]):\n", + " continue\n", + " att = es_boot.att[i]\n", + " se = es_boot.se[i]\n", + " lo, hi = es_boot.conf_int_lower[i], es_boot.conf_int_upper[i]\n", + " pval = es_boot.p_value[i]\n", " sig = \"*\" if pval < 0.05 else \"\"\n", - " print(f\"{event_time:>12} {att:>10.4f} {se:>10.4f} [{ci[0]:>8.4f}, {ci[1]:>8.4f}] {pval:>10.4f} {sig}\")" + " print(f\"{event_time:>12} {att:>10.4f} {se:>10.4f} [{lo:>8.4f}, {hi:>8.4f}] {pval:>10.4f} {sig}\")" ] }, { @@ -1233,16 +1209,16 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.463732Z", - "iopub.status.busy": "2026-08-10T22:40:30.463680Z", - "iopub.status.idle": "2026-08-10T22:40:30.519862Z", - "shell.execute_reply": "2026-08-10T22:40:30.519537Z" + "iopub.execute_input": "2026-08-18T20:30:20.413626Z", + "iopub.status.busy": "2026-08-18T20:30:20.413575Z", + "iopub.status.idle": "2026-08-18T20:30:20.452109Z", + "shell.execute_reply": "2026-08-18T20:30:20.451739Z" } }, "outputs": [ { "data": { - "image/png": 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ItqOveYzGHc3iYz+ubIURtd2hfFNmME3t32iaBmqgaYpndMdNorPOOqvX+dHv+bzzzsv7a2UrhciTuGkVaYp9o9yaZFFvx1CLpu/R9Dy6FFSmPVrzxA28GKUdYLgQeAPUqKjBjEd0RSAdzU7/53/+p9v8eNxRPCYqag/LtcYXXXRRrgEezHOlowl7uYl6f/1sP/vZz+YAJAY6e+ihh7rVrMZAa9FMPAaE6mudMfhSBCURvIf4u+fjjiI4jT6/EZxEIBbLRM12T1E7HkFqLBdNfCsfLVZNEUzFY6IioIg+8CGCzWi1EM2Co0YzauZjMLHoF3/33XfnZeLGQgycFfPKzwiPPIwbFZGvfZVL9LmNJvYxKFv5hkXk+Ze//OV8I6Pc97dokdZPfvKTOQiMJtMh0h2D5EUNf6Qx+iRHK4zow1zeX6NP/Kmnnto1qFh5XdH/OZrmL6jf9xtN02DWNZA0xY2XeNRdDHwW3S/KLTViX77llltyzW+UWc9WIzFuQrReOO2003J3ksrHbC3K7RhqUaMdjzmLG07xCLnIh46OjtxCJvb9wRynAOqdpuYAQ6yvEbRD9O38zne+063WO2ozo4asZy1yXKDHxX30D40atqhFjNqyGNV5MDXAESxErVo0WY9/+6pBi+A/+mrGQFAx0FN8V2xHBI0RVEf/03K/1N7WueWWW+bAI4LCCBoieIj1RO1sZQBywgkn5Gcnx3ZFk90ILCqfy1xZSxhN8OP51j2b2w72Od6DKZMFiT7McTMgnmUceRTbEP3SIxgvNweP0erLz9iOcotgLpopx02GcjlHrWcEKzG4XOTT5z73uV6/L56ZHvkV2xvrjCbscZMmAvtyP+KhENsbeRjbEDdiIsCKFhARTJYHQIsbLdEaI5qbx7IRVMYNndiPYxCueN58jFYerTziZko0T478LDJNAzGYNMXNjnieeDxtIJqGx82UKJNo9bDnnnvmILpnN5Aot9iPzz///F5/u4tqO6ohBkuMG3Zx3IjfUXnMhrg5Eb8tgOGiKZ4pVu1EAAwHEVwtaOClCMKiz2dZBG0xOnNM622QtLJYbwRslYOQhRhcK2rpevb/feaZZ3KNaLkPcJwKokY2AoCB9JWO5uFR0x3pi+/srZ9pX+uMGuBIVwwEFZ+L7YugonKZWH8MQhaBWdQExqO2Ir3lEZNDPOopgpgY/b1yVOXKfIsAuL/tGUyZlNcZtdrlID1qlyOoKt9wqBS1urHuqL3sa1CquPkQy/SVh/GdMT+2ob/yr1xXbHPP7+svnX2JfSTSVLk/hsiDuKHQ12PiouyiP3ekobLmtnK/iLKNdJbzMfaTmF5+ZFtsS3mZ2Afi5kzUnlZ+Z+xDUX6V21Quz7hRUxnc9pem3tYdZRfNwmMfLedlzzQNRATIkZ7yftyfaN3w0ksv5e3pq2l7f9uxoHLpS3wujh3lvC+L74nvK0+P91FGlU3k46Zb7FuVgwLG7zvKM276VO7T5cHWoswNqgYMRwJvAOpOBDRRmx4BSjyiCgCglmlqDkBdiSbX8Ri1qImLRywBANQ6Nd4A1JVoyhrNW6PJ62AfbwQAUA0CbwAAACiQx4kBAABAgQTeAAAAMJwHV4tHesTjKuJxNH09XgMAAACGUjx2sa2tLT8mMR7pWNeBdwTdTzzxRLWTAQAAAPNZZZVV0pJLLpnqOvCOmu7yxowdOzbVwx2PUM8j7cZ2PPnkk2nllVfWyqBGKJPaojxqSyOVR6lUyv/WeyuveM76I488klZfffXU3Nxc7eQMe8qj9iiT2qI8aktHHZ1DZs+enSuJyzFrXQfe5QuPCLpbW1tTrV/8RXoj6K7nwDt29nKe1/rOPlwok9qiPGpLI5VHBN7xGjNmTN0H3iHO2/VeJo1AedQeZVJblEdt6ajDc8hAztn1e1YHAACAOiDwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAI4tcOQAAAPRmVltpvmkdHaU0p70pz2tu7j6/taUp1SuBNwAAAENu0qEv9TFnpZTSK/NNfeSMpVK90tQcAAAACqTGGwAAgCF375Qlu72f3VZKkw97Of99y4mLp3GtjROuNs6WAAAAUDda++mzPbalqa77dPekqTkAAAAUSOANAAAAjdrU/N57700PPPBAWmKJJdIWW2yRxo0bV83kAAAAQOPUeB977LHpwAMPTI888ki66KKL0vbbb5+effbZaiUHAAAAGqfG++abb04XXHBBDrjXXHPNPO30009PzzzzTFphhRWqkSQAAABonMD7yiuvTFtuuWVX0B3233//aiQFAAAAGi/wjubl73//+9OTTz6Zbr311rT00kunzTffPI0ePbrPz5RKpfyqZfWQxoEob0OjbE8jUCa1RXnUlkYqj/I21Pu2NFKZNALlUXuUSW1RHrWjVHHOqIdzyGDSV5XAe/r06enOO+9Ml112WZo0aVL69a9/nU455ZR07rnnpsUXX7zXz8yYMSO1t7enWs/4uXPnpqampvyqVx0dHV153tzcXO3koExqjt9IbWmk8ihfZJTPJfWqs7Mz/ztt2rQ0YoQHqFSb8qg9yqS2KI/aMbvt9UB2+vRpqbO9ts/rbW1ttR14x4XRc889ly688MI0atSofNG02267pTPOOCMdfvjhvX4mRjxvbW1Ntf6jnTNnTsME3pHn9X4R2yiUSW1RHrWlkcqjHHiPGTOmrgPWcplMmDCh7sukESiP2qNMaovyqB2jcuD9cv57/PgJaXxrVR/CtUCzZs0a8LJV2ZLll18+rbrqqjnoDnFSXn/99dOjjz7a52fqIZgtp7Ee0tqfctrrfTsaiTKpLcqjtjRiedT7tjRimdQz5VF7lEltUR61o6nilFEP55DBpK8qt9M322yzdPvtt+fa4RB39++777602mqrVSM5AAAAUJiq1Hjvueee6aqrrkof+9jH0tZbb53uuOOO9NJLL6VPfepT1UgOAAAANFbgHaOXn3POOTn4fuyxx9IOO+yQdtppp9w/DwAAABpJ1XqrR//unXfeuVpfDwAAAEOifodMBQAAoCG0tZfS725//fFcR5w5M1365zl5eiOo7fHZAQAAaGjX3duWjjprRpo26/Ug+4ap7emP97Wn486bmU7Yd1zaalJLqmdqvAEAAKha0H3IlOlpekXQHTr/+zamHzxlel6ungm8AQAAGHJt7aVc051K+b9e5emllJer52bnAm8AAACG3FV3teXm5QsKp2N+LPf7u+q31lvgDQAAwJC79p65aUTTwJaN5a65Z26qVwJvAAAAhtwrMzq7+nIvSCz3yszOVK8E3gAAAAy5JcaNGFSN9xKL1W/4Wr8pBwAAoG5ts+7oQdV4b7vu6FSvBN4AAAAMuR3Wb0kTWpvSgiq9Y34st/369fssb4E3AAAAQ65lVFM6Yd9xObLuK/jO05tSXi6Wr1cCbwAAAKpiq0kt6dRDx6fxrd2D6nLf75j+40PH5+Xq2chqJwAAAIDha+tJLenWk0any25rS0f/fEaetsXEUbkpejQvr+ea7jI13gAAAFRVy6imtOMGr9dqn7jfYmmXyWMaIugOAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAABN4AAABQn9R4AwAAQIEE3gAAAFAggTcAAAAUSOANAAAABRJ4AwAAQIEE3gAAAFAggTcAAAAUaGSRKwcAAIDezGordXs/u+J9/N3c3H1+a0tTqlcCbwAAAIbcpENf6nPeJke8Ot+0R85YKtUrTc0BAACgQGq8AQAAGHL3TllyvmkdHR1p6tSpaeLEiam5ublhSkXgDQAAwJBr7aXPdkdHUxozqpTnNTfXb5/unjQ1BwAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAo0MgiVw4AAFArZrWV5pvW0VFKc9qb8rzm5u7zW1uahjB1NDKBNwAAMCxMOvSlPuaslFJ6Zb6pj5yxVOFpYnjQ1BwAAAAKpMYbAAAYFu6dsmS397PbSmnyYS/nv285cfE0rlV4RDGqtmfdcsst6emnn+427b3vfW9aaaVo5gEAALBo9ddne2xLkz7dNF7gfcIJJ6RlllkmLb/88l3T1l577WolBwAAABon8J47d2567LHH0imnnJLe9ra3VSMJAAAA0LiDqz388MNpzJgx+e/f/va36Y477kidnZ3VSAoAAAA0Xo33gw8+mNra2tIXv/jF9Pa3vz3ddtttadVVV02nn356amlpqUaSAAAAoHEC78UXXzwddNBB6eCDD07Nzc3p1VdfTTvvvHM688wz0yGHHNLrZ0qlUn7VsnpI40CUt6FRtqcRKJPaojxqSyOVR3kb6n1bGqlMGoHyqD3KpHZUHqMcs2pDqY7OIYNJX1UC7+23336+QHy77bbLTc77CrxnzJiR2tvbU61nfPRfb2pqyq961dHR0ZXncWOE6lMmtUV51JZGKo/yRUb5XFKvyt3Hpk2blkaMqEqvNpRHTfMbqR3xOLGy6dOnpc72+j6PNILOOjqHRCvumg68r7322jyi+cSJE7tN7++Cady4cam1tTXV+k4yZ86chgm8I8/r/SK2USiT2qI8aksjlUc58I5xUGr9YmMgZTJhwoS6L5NGoDxqjzKpHaNy4P3ac7zHj5+QxnuOd9V11NE5ZNasWbUdeN98883p0UcfTeeee25+H3f2//jHP6Y99tijz8/UQzBbTmM9pLU/5bTX+3Y0EmVSW5RHbWnE8qj3bWnEMqlnyqP2KJPaUXmIcsyqDU11dA4ZTPqqEnhH3+4PfehDuVn5euutl6666qp8R2PvvfeuRnIAAACgMFVpx7bccsulK664Im2wwQbp+eefT7vttls677zzuh4xBgAAAI2iKjXe4U1velP65Cc/Wa2vBwAAgCFRvyO3AAAAQB0QeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAMNOW3sp/e72tq73R5w5M1365zl5OjTM48QAAACq4bp729JRZ81I02a9HmTfMLU9/fG+9nTceTPTCfuOS1tNalE4LDJqvAEAgGEVdB8yZXqaXhF0h87/vo3pB0+ZnpeDRUXgDQAADAvRjDxqulMp/9erPL2U8nKanbOoCLwBAIBh4aq72nLz8gX14o75sdzv71LrzaIh8AYAAIaFa++Zm0Y0DWzZWO6ae+YWnSSGCYE3AAAwLLwyo7OrL/eCxHKvzOwsOkkMEwJvAABgWFhi3IhB1XgvsZhwiUXDngQAAAwL26w7elA13tuuO7roJDFMCLwBAIBhYYf1W9KE1qa0oErvmB/Lbb++Z3mzaAi8AQCAYaFlVFM6Yd9xObLuK/jO05tSXi6Wh0VB4A0AAAwbW01qSaceOj6Nb+0eVJf7fsf0Hx86Pi8Hi8rIRbYmAACAOrD1pJZ060mj02W3taWjfz4jT9ti4qjcFD2al6vpZlFT4w0AAAw7EVzvuMHrtdon7rdY2mXyGEE3hRB4AwAAQIEE3gAAAFAggTcAAAAUSOANAAAABRJ4AwAAQIEE3gAAAFAggTcAAAAUSOANAAAABRJ4AwAAQIEE3gAAAFAggTcAAAAUSOANAAAABRJ4AwAAQIEE3gAAAFAggTcAAAAUaGSRKwcAAKgVs9pK3d7Prngffzc3d5/f2tI0ZGmjsQm8AQCAYWHSoS/1OW+TI16db9ojZyxVcIoYLjQ1BwAAgAKp8QYAAIaFe6csOd+0jo6ONHXq1DRx4sTU3NxclXTR+ATeAADAsNBbn+2OjqY0ZlQpz2tu1qebYmhqDgAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBPE4MAAAKMqutNN+0jo5SmtPelOc1N5cW+LgroP4JvAEAoCCTDn2pjzkrpZRemW/qI2cspSygAWlqDgAAAAVS4w0AAAW5d8qS3d7PbiulyYe9nP++5cTF07hWl+MwHPilAwBAQfrrsz22pUmfbhgmNDUHAACAAgm8AQAAoEACbwAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAACiQwBsAAIZAW3sp/e72tq73R5w5M1365zl5OtDYRlY7AQAA0Oiuu7ctHXXWjDRt1utB9g1T29Mf72tPx503M52w77i01aSWqqYRKI4abwAAKDjoPmTK9DS9IugOnf99G9MPnjI9Lwc0ppoIvG+88cb09NNPVzsZAACwSEUz8qjpTqX8X6/y9FLKy2l2Do2p6oH3XXfdlQ488MA0derUaicFAAAWqavuasvNyxfUizvmx3K/v0utNzSiqgbebW1t6eijj05NTU3VTAYAABTi2nvmphEDvNSN5a65Z66SgAZU1cHVvv/976fVVlsttbe3VzMZAABQiFdmdHb15V6QWO6VmZ1KAhpQ1Wq877///nTJJZekY489tlpJAACAQi0xbsSgaryXWKzqPUGBRqnxjhruL3/5y+nII49MSy+99IA+UyqV8quW1UMaB6K8DY2yPY1AmdQW5VFbGqk8yttQ79vSSGXSCJRHdW09aXT6w91zB1zjvc26o/1uhpjfSG0p1dE5ZDDpq0rg/ZOf/CQtu+yyaddddx3wZ2bMmFHzTdIj4+fOnZv7rNdzv/WOjo6uPG9ubq52clAmNcdvpLY0UnmULzLK55J61dn5WlPZadOmpREj1N5Vm/Koro3fUUrjx6Y0Y3bfo5qH+MWPG5vS5NVmp1dfnTOEKcRvpLZ01tE5JMYsq9nAOx4bdtppp6WDDjooXXzxxXnazJkz0x133JHe/OY3p8mTJ/f6uXHjxqXW1tZU6zvJnDlzGibwjjyv94vYRqFMaovyqC2NVB7lwHvMmDE1f7ExkDKZMGFC3ZdJI1Ae1XfCfnPzc7yb+nikWL5qbIrlxqdllho99Akc5vxGaktHHZ1DZs2aVbuBd2TkTjvtlJ555pn8CnFn/7HHHksPP/xwn4F3PQSz5TTWQ1r7U057vW9HI1EmtUV51JZGLI9635ZGLJN6pjyqb+tJLenUQ197Tnc8MqyyT3c0Lx/f2pRO2Hdc2mpSS1XTOVz5jdSWpjo6hwwmfUMeeK+yyirp+OOP7zYtarv33HPPtOOOOw51cgAAYEiC71tPGp0uu60tHf3zGXnaFhNHpR3Wb0nbr9+SWkbVdoABvDE10Y5tu+22SyuuuGK1kwEAAIWJ4HrHDV6v1T5xv8XSLpPHCLphGKjqc7zLjjrqqGonAQAAABq3xhsAAAAalcAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAABo9FHNAQCgEc1qK3V7P7viffzd3Nx9fmuL53lDIxJ4AwBAQSYd+lKf8zY54tX5pj1yxlLKAhqQpuYAAABQIDXeAABQkHunLDnftI6OjjR16tQ0ceLE1NzcLO9hGBB4AwBAQXrrs93R0ZTGjCrlec3N+nTDcKCpOQAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgUYWuXIAAIbOrLbSfNM6OkppTntTntfc3H1+a0vTEKYOYPgSeAMANIhJh77Ux5yVUkqvzDf1kTOWKjxNAAyiqfmvfvWrNGPGDHkGAAAARdR4n3baaWnrrbdO48aNS4vKY489lp555pm0+uqrp7e85S2LbL0AAMPRvVOW7PZ+dlspTT7s5fz3LScunsa1auwIUA1VO/p+7WtfS9dff31aY4010l/+8pd0wAEHpIMPPrhayQEAqHv99dke29KkTzdAPQTeTz31VJo5c2a/y6y88spp5Mj+V3vNNdfkoPvKK69M48ePz4H3XnvtlXbeeee0wgorDCZJAAAA0DiB98c//vEFLnPjjTem5ZZbrt9l/vOf/+Ta7Qi6w/LLL59KpZI+5AAAAAzvwPvss89OSy7Zve9QT0stteDRMXfffff870svvZSuu+66dO655+Zp0ewcAAAAhm3gveqqqy6wNnswnn766fTggw+muXPn5iB8+vTpXbXgPUWNeLxqWT2kcSDK29Ao29MIlEltUR61pZHKo7wN9b4tjVQm9a4y/5VH7fAbqS3Ko7aU6ugcMpj0VXVoy0mTJuVXW1tb7t/9k5/8JB1xxBG9LhuPMmtvb0+1nvFxE6GpqSm/6lVHR0dXnjc3N1c7OSiTmuM3UlsaqTzKFxnlc0m96uzszP9OmzYtjRgx4CeXUoAY1bxs+vRpqbO9vn8jjcJvpLYoj9rSWUfnkIhjF3ngfeCBBy6yR4ndfvvtaZlllsk16KGlpSW95z3vSY8++mifn4nvbm1tTbW+k8yZM6dhAu/I83q/iG0UyqS2KI/a0kjlUQ68x4wZU/MXGwMpkwkTJtR9mdSztvZSunpqXBS+NjDuN37TnLZff0zaYf3RqWVU/V6nNAK/kdqiPGpLRx2dQ2bNmlXbNd5XXHFFeuGFF3INdzlz77vvvrTZZpv1+Zl6CGbLaayHtPannPZ6345Gokxqi/KoLY1YHvW+LY1YJvXmunvb0lFnzUjTZr1e433j/fPS9VNnpG/9uimdsO+4tNWklqqmcTjzG6ktyqO2NNXROWQw6Rvw7fTTTjttkY06Hs/svvvuu9MxxxyTLr744lybHncL9t9//0WyfgCA4Rx0HzJleppeEXSHzv++jekHT5melwNgaFSlHduKK66YLr/88rTEEkukO+64I2200Ubpt7/9bX4PAMDCNy+Pmu5Uyv/1Kk8vpbxcLA9A8QbV1Pypp55KM2e+1k+oLyuvvHIaOXLBq1122WXTF77whcF8PQAA/bjqrrZuzcv7EkvEcr+/qy3tMnmMPAWopcD74x//+AKXufHGGxfpI8cAABiYa++Zm0Y0vd6svD+x3DX3zBV4A9Ra4H322WenJZdcst9lllpqqTeaJgAAFsIrMzoHFHSHWO6Vma89tgeAGgq84/FfarMBAGrTEuNGDKrGe4nF6vexdQD1ZJEdbdvb29OVV16Zpk+fvqhWCQDAIGyz7uhB1Xhvu+5o+QtQS4F3PPJr3Lhx803/5z//mU4++eS0xRZb5MHSBvMQcQAAFp0d1m9JE1qb0oKeLBvzY7nt1/csb4Caamq+1157df3d2dmZbr755nTeeeflwdTiMWC77LJL2m233fJo5QAADL2WUU3phH3H5ed0N/XxSLEclDelvFwsD0CN9fF++eWX04UXXpjOP//89Nxzz6XJkyensWPHposuuigtv/zyxaUSAIAB2WpSSzr10Nee0135aLFy3+/xra8F57EcADUWeB9zzDHp4osvTquvvnraZ5990o477phHMN9ss83SiBEG5gAAqBVbT2pJt540Ol12W1s6+ucz8rQtJo7KTdGjebmaboAaDbyjSfnEiRPTxz72sbT55pv32t8bAIDaEMH1jhu0dAXeJ+63WBrfOqjGjgAsIgOuqo7m5VG7/YMf/CBtvPHG6XOf+1z6wx/+kPt7AwAAAG8w8I5B02Jk86uvvjqdeeaZqbW1NR155JHpxRdfTFOmTElTp04d6KoAAABg2Fioztnvec970vHHH59uueWW9M1vfjM9/PDDeUTz7bffPr3yyiuLPpUAAABQp95QR5/o57377rvn19///vc8urmm5wAAAPC6RTbCxmqrrZaOOuqoRbU6AAAGaVZb9yd3z654H383N3ef39riOd4AQ8HQlgAADWLSoS/1OW+TI16db9ojZyxVcIoACB7ADQAAAAVS4w0A0CDunbLkfNM6Ojry02cmTpyYmpubq5IugOFO4A0A0CB667Pd0dGUxowq5XnNzfp0A1SDpuYAAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBBN4AAABQIIE3AAAAFEjgDQAAAAUSeAMAAECBRha5cgCgsc1qK803raOjlOa0N+V5zc3d57e2NA1h6gCgNgi8AYCFNunQl/qYs1JK6ZX5pj5yxlJyG4Bhp6qB99NPP52effbZtOKKK+YXAAAANJqqBN7z5s1Lhx12WLrzzjvTaqutlh566KG05ZZbpuOPPz6NGKHbOQDUi3unLNnt/ey2Upp82Mv571tOXDyNa9W4DgCqcjY877zz0sMPP5yuueaaNG7cuPT888+nD37wg+niiy9OH/nIR5QKANSJ/vpsj21p0qcbAKo1qvns2bPTvvvum4PusOyyy6Z11103/e1vf1MoAAAANJSq1HgfcMAB3d7PnTs3PfDAA2nTTTetRnIAAACgMDXR8erEE09MY8eOTbvsskufy5RKpfyqZfWQxoEob0OjbE8jUCa1RXnUlkYqj/I21PO2VKa7nrejkTTSb6RRKJPaojxqS6mOjlmDSV/VA+9TTjkl9/U+55xzUmtra5/LzZgxI7W3t6daz/iovW9qasqvetXR0dGV583NzdVODsqk5viN1JZGKo/yRUb5XFKPYnC1sunTp6XO9vouk0bQ2dmZ/502bZpBbGuEMqktyqO2dNbRMautra32A+/I0G9+85vprrvuSueff37u592f6A/eX2BeC2Kb5syZ0zCBd+R5vV/ENgplUluUR21ppPIoB95jxoyp+YuNvozKgfdro5qPHz8hjTeqec38RiZMmFD3v5FGoUxqi/KoLR11dMyaNWtW7T9O7Mgjj0wvvvhiOvfcc9P48eMX+Jl6CGbLaayHtPannPZ6345Gokxqi/KoLY1YHvW8LZXJruftaCSN+Bupd8qktiiP2tJUR8eswaSvKoH3SSedlK6//vp03HHH5RrvsuWWWy6ttdZa1UgSAAAAFKIqgfcLL7yQNthgg3TZZZd1m77hhhsKvAGgTrW1l9Lvbn+9v9sRZ85M26/fknZYvyW1jKrtWgsAKFLVarwBgMZx3b1t6aizZqRps14fXO2Gqe3pj/e1p+POm5lO2Hdc2mpSS1XTCADVUp8jtwAANRV0HzJleppeEXSHzv++jekHT5melwOA4UjgDQC8oeblUdOdSvm/XuXppZSXi+UBYLgReAMAC+2qu9py8/IFhdMxP5b7/V1qvQEYfgTeAMBCu/aeuWnEAMdNi+WuuWeu3AZg2BF4AwAL7ZUZnV19uRcklntlZqfcBmDYEXgDAAttiXEjBlXjvcRiLj0AGH6c/QCAhbbNuqMHVeO97bqj5TYAw47AGwBYaDus35ImtDalBVV6x/xYbvv1PcsbgOFH4A0ALLSWUU3phH3H5ci6r+A7T29KeblYHgCGG4E3APCGbDWpJZ166Pg0vrV7UF3u+x3Tf3zo+LwcAAxHI6udAACg/m09qSXdetLodNltbenon8/I07aYOCo3RY/m5Wq6ARjO1HgDAItEBNc7bvB6rfaJ+y2Wdpk8RtANwLAn8AYAAIACCbwBAACgQAJvAAAAKJDAGwAAAAok8AYAAIACeZwYALDQZrWVur2fXfE+/m5u7j6/taX7s74BYDgQeAMAC23SoS/1OW+TI16db9ojZywltwEYdjQ1BwAAgAKp8QYAFtq9U5acb1pHR0eaOnVqmjhxYmpubpa7AAx7Am8AYKH11me7o6MpjRlVyvOam/XpBgBNzQEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMAbAAAACiTwBgAAgAIJvAEAAKBAAm8AAABo5MB73rx56YEHHqh2MgAAAKAxA+/vfe976eijj652MgAAAKCxAu+ZM2emr33ta+nMM8+sVhIAAACgcQPvPffcMz399NNp//33r1YSAAAAoHED7+OPPz797Gc/S8stt1y1kgAAAACFG5mq5J3vfOegli+VSvlVy+ohjQNR3oZG2Z5GoExqi/KoLY1UHuVtqPdtaaQyaQTKo/Yok9qiPGpLqY7OIYNJX9UC78GaMWNGam9vT7We8XPnzk1NTU35Va86Ojq68ry5ubnayUGZ1By/kdrSSOVRvsgon0vqVWdnZ/532rRpacSIqo/jOuwpj9qjTGqL8qgtnXV0Dmlra2u8wHvcuHGptbU11fpOMmfOnIYJvCPP6/0itlEok9qiPGpLI5VHOfAeM2ZMzV9sDKRMJkyYUPdl0giUR+1RJrVFedSWjjo6h8yaNavxAu96CGbLaayHtPannPZ6345Gokxqi/KoLY1YHvW+LY1YJvVMedQeZVJblEdtaaqjc8hg0le/t9MBAACgDlQ98F522WXT2muvXe1kAAAAQCGq3tR82223zS8AAABoRFWv8QYAAIBGJvAGAACAAgm8AQAAoEACbwAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAABB4AwAAQH1S4w0AAAAFEngDAABAgUYWuXJg8Ga1leab1tlRSnPam9LstlIa0dx9fmtLk2wGAIAaJvCGGrPR4TP7mLNSSmnOfFOn/mhc4WkCAAAWnqbmAAAAUCA13lBjbjtpsW7vo3n5ll+elf++7riWtNhYP1sAAKgnruChxvTXZ3vs6CZ9ugEAoM5oag4AAAAFEngDAABAgTQ1B6BueNweAFCPBN4A1A2P2wMA6pGm5gAAAFAgNd4A1A2P2wMA6pHAG4C64XF7AEA90tQcAAAACiTwBgAAgAIJvAEAAKBAAm8AAAAokMHVAPoxq60037TOjlKa096UZreV0ojm0oAH/wIAYHgSeAP0Y6PDZ/YxZ6WU0pz5pk790Tj5CQBAN5qaAwAAQIHUeEMNa2svpavuau96/+VfzE3brldK71t3ZGoZpUnzULjtpMW6vY/m5Vt+eVb++7rjWtJiYx1GAQDonytGqFHXT52XvnrOnDRt9uvTbvprZ7rhgbb03Qva0nH7jElbvNtPuGj99dkeO7pJn+4qcmMKAKgXmpoPczFwVM9X1OiVB47qOY+hC7r/9/Q5aXpF0B06/1sEMf3zP52Tl4PhKPb9rb88M339vLndbkwd/Yu2PP2G+/02AIDaobpsmDNwVG3W4kVNdyrl/3oV05tKKS933bcX0+ycYXljqucPpOeNqVP2H5O2nOg0BwBUnxpvqDF/uGdebl6+oPYFMT+Wu+YeNXsMHwO9MRX/i+VieQCAalMVMMwZOKr2/PG+eWlE0+u1d/2J5a67b17aaYNRQ5E0qJkbU2kQN6b8PgCAalPjPczFwFGVr7EVA0mVB46qfFG8V2eVBhR0h1gulofhdmNqIMo3pgAAqk3gDTVm8damQQUWsTwMF25MAQD1SOANNWardUYOqsZ763X0GGH4cGMKAKhHAm+oMe9bd2SaMDalBdVjx/xYbtt1Bd7VfG705Xe0G8BrCLkxBQDUI4E31JiWUU3puH3G5Mi6r+A7T29KeblYnuJ5bnRtcGMKAKhHAm+oQVu8e2R+BvH4sd2nl/t+x/TvHzAmL8fQPTc6ng/d33OjYzmK5cYUAFCPBN5Qo7acODJd9+3F0jEfa+mattm7RqRv79OSpwu6h4bnRtceN6YAgHoj8IYar93b4X9er9X+9j6j8zOJNS8f+udGlwbx3GiK58YUAFBPqhp4z5gxIz366KNp1qxZ1UwGQJ88N7p2uTEFANSLqgXeP/vZz9Kmm26aDjnkkPzv5ZdfXq2kAPTJc6MBAHijqjIy07333ptOPvnk9Jvf/Catueaa6aabbkqf+cxn0vrrr5/e8pa3VCNJAP0+N3ogz1aP5WJ5AACoeo131G5vscUWOegOm222WXrnO9+Zrrrqqmokh//yjGKYn+dGAwBQl4H3I488klZbbbVu097+9rfn6VSHZxRD7zw3GgCAumxqPn369DR+/Phu0xZbbLH07LPP9vmZF198MY0aNarr/ZgxY9Kb3vSmNG/evDyvp3KT9X//+9+pvb2927wlllgijR07Ns2cOTNNmzat27zRo0enJZdcMnV2dqbnn39+vvUus8wyqbm5Ob388supra1tvm0YOXJkmjNnTnr11Ve7zYu0x3pDrLdU6t5uNebFMvG52bNnz7feyK/4vv/85z/d5kVall566a486ujo6DY/8qilpSXneWxvpciDxRdfPF17T1v64lnlPGrq5RnFpfT502anY3abnSav3tGVh1EGsc5Yd6X4vvjeSEtvZRN5OGLEiJyHc+fO7TZvwoQJqbW1NedBf3n4r3/9a771LrXUUjn/X3nllVwGlcaNG5dfveVhfCY+G1544YVc9pXe/OY35/2itzyMtEaaYx976aWXus1rampKyy67bNd+GPtqpXIexiCD8aoU02N+5OHz/4o8HN+170wfMyKvN9bfXx7GoIW97d+xPbH/9bZ/x74U+1R/eRjTY35fedjf/h3p6TmYYnn/ju2I7akU+0nsL33lYXn/7i8P4zPx2Z6WW265/G+UW89jRPwu4vdRzsPDd2pOx14wNv86ShW/kbKm/455fvhOs1Nz05hUKjXn/aznMSK2M7a3tzys5WPEgvbv3vJwqI4Rc/Ls134fL738clps7NJ1f4yIsi+fi8rHiJ55WD4HRh5Gmnrbv2P98Z098zDKtHyM6HmcLX9vpKG3PCyfAyOPeuZh7IPlY0RsT6z7n//8Z14+8rC8H8Z6e+7fkb/l/bu3Y0TkYWxHzzyM/aSch5EPPffvKJvy/t3bMaLWriPKeRj7d2/H2XIePvfcc/Ott5yH8bmex4jY1tDb9UllHkZ6e9u/Iw9jO3s7RkReRP70dpwt52Hkb2/nwPh8b/t3fF98b1/7d6Q30t3b/h37SuwzveVh5E/5t9xbHkb+Rj73tn9HuZSPsz3PVVGe5XNVb3kY+0PsF5V5GNsWZRFpjG3tLQ/jN1w+V/WWh7H/1usxorfjbDWPEVE2lces4XaMKOdhb/t3NY4RHR0duTxjP4h9u5aPET3LvuYC78iInjt+FGJlYN3TxRdf3G3HXWONNdL222+fM+znP//5fMt//vOf7/pczwPDdtttl5u533fffemGG27oNm+llVZKu+66a97xTj/99PnWu//+++eD0RVXXJEef/zxbvM22WSTtPbaa6d//OMf6Q9/+EO3eVGIu+++e/77nHPOmW+n3WOPPfIOFP3d//a3v3Wbt+6666bJkyfnGxO//e1vu82LHecTn/hE/jv6zPfc4XfZZZe0wgorpNtuuy3dc8893eattdZaaeNNtkxf+1Xpv8FE731TX5tXSt++cET60Aq/SM1NHel973tfbrUQ/fX/9Kc/dVt+lVVWSe9///vzDv2LX/xivvV9+tOfzgedq6++Oj399NPd5sVAe+9+97vTww8/nK677rr5fkQf/vCH89+9rXevvfbKJ4vrr78+j5ZfKcYP2GCDDdJTTz2Vfve73833A9x7773z37/+9a/nO1F86EMfyieo2M7YZypFeUdXiThgXHDBBd3mxf4c+0u45JJL5jvJ7LDDDmnVVVdNf/nLX9Ltt98+XwuQ2E/jZHruueemlA7sSt/IEfPSgQcemA/KV155ZT5JVIpuHNF148EHH5xv/15++eXTBz/4wfz76y0P99lnn3xAj7x/7LHHus3bcMMN0//8z//k/b5nt5A4Me25557570hvz4PQbrvtlg9Wt9xyS3rggQe6zVtnnXXSe9/73vw7jd9rpTjp7bvvvvnvCy+8cL6T10477ZR/s3fccUe66667us17xzvekbbddtt8Iv3Vr34137bGwI4h9oeeJ76tt946H2Puv//+dPPNN+dpmy21SvrzS1unuaXXLmBfE7/jEWlUU1uavOR16ZHbnkgvr/WpfKK55ppr0hNPPNFtvRtvvHGaNGlS+vvf/143x4gtt9wyn4Rj3+t5HD/ooIPy3/GdPU+oQ3WMmNc5suv3cfXvf5/2/OiuDXOM+MAHPpDe9ra3pTvvvHO+PIy83XHHHfPFyFlnnTXfth566KH54uDSSy+d76Z27N+Rrvgt9jzOxr7wkY98JF8A9XYOjN9jXPjFMSD2457793ve8558DiwPmFr+/cS++/GPfzz/ffbZZ893IRTHj7jYjLKZOnXqfPt35GFcBMU+XCl+awcccED++7zzzpsvSIjj3corr5z3757H2Vq8joj9e7311sv7ZxzfK8Ux9GMf+1j+O8q853VU7KNxMR9l+te//rXbvDh2xwVuHNdjX6sUx/z99tsv/x3Hyp43KOK8u+KKK6Zbb711vuPsu971rrTNNtvki9tf/vKX3ebFOSrG7ykfv3sGLnEMiOP03Xff3bWflMW5ceedd84BVm95GMeeuPCO/Sx+sz3PgXFeeeihh/IxpFL8Tj/60Y/mv3tbbxwrI0iI43McY3qeAzfaaKP05JNP5t9VpTiufPKTn+w69vQMauLYHvkfx++ex9nY/7baaqscPMQ+XCmOgwcffHD++/zzz58vIGqEY0RZLRwj4lqicl8cTseIOAfGtdgzzzyTLrroopo4Rqy33nr5ui+uI2r5GBHXCLH/DURTqectpSEQmREZdtRRR3VN+8IXvpB3hq985Svdlo0MjYvM8l2aslqu8Y7p9VLjfdPDrekr53Q/uPXnyJ1np63fPU+N9xDWeD/97ItplxNfq9G75PBXU6sa7yGv8S6bOy+lG/82Jv2/y147Fm30jva02Vrz0qZrzUujR3ZvNaDGe+hqvMu/j8v/b1ZaaQU13rVS4x0XhhFYqfGujRrvuICPQECNd+3UeMdvJAIANd7Vr/GO303cFCkfs2o11hhONd7PPPNMvlETaa31Gu/YtqisiHNrzQXeP/3pT/Odgri7ESLT4m5fBOJxB7S3wHsgG1NtsSNGIcTFTrzqwRdOn52un9ox4BGbt5zYnE7ef+xQJI3/mtVWShsd/tqB7E8njEnjWqvSUAXlUZMa7fcRp+R4xQVfXDTUqzivRy1FtPAoX8SiPPAbqVWOWbWlo47OIYOJVatyVo8mKtF88JhjjslNYw4//PB8ByeahTK0PKMYqLdAu/I1u+31u4az53afFy8AgFpQlaqBaBoQ/UBPPfXU9MMf/jA3fYr+HP318aYYnlEM1JNy7XZvtv5KNMnr3ixv6o/GDUGqAAD6V7U2eTF41EknnVStr6fiGcXX3de9v0tfojn61uvUdzNOAACAoSaKGubiGcXfvaAtTZ8d45b3LXqsjx+b0rbr2mWA6rntpMXmm9bZ0ZEefuSRtMbqq6cRNd4XDAAYnup35BYWiZZRTem4fcbkyLqv4eDy9KaUl4vlAaqltaVpvtfYlqY0ZlQp/9tzHgBALRB4k7Z498h0yv5jco12t53jv9esMf37B4zJy1E8g0cBAEBjEUmRbTlxZLru24ul3905L3393NcGJ9rsXSPS+9YblZuXq+keOgaPAgCAxqLGmy4RXO/wP6/fi/n2PqPTThuMEnQzrGmBAADAG6XGG2qMwaNqixYIAAC8UQJvqDG9DQjV0fH64FHNzQaMAgCAeiLwBuiHFggAALxRAm+AfmiBAADAG2VwNQAAACiQwBsAAAAKJPAGAACAAgm8AQAAoEACbwAAACiQwBsAAAAK5HFiw9ystlK397Mr3s+eW0ojmksLfLQSAAAAfRN4D3MbHT6zz3lbf6UtpRSv10390bghSBUAAEDj0NQcAAAACqTGe5i77aTF5pvW2dGRHn7kkbTG6qunEc3NVUkXAABAoxB4D3O99dnu6GhKY0aV0tiWptTcrE83AADAG6GpOQAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgUamGtfZ2Zn/nT17dqqHtLa1teW/m5qaUr2qzPMRI9ybqQXKpLYoj9rSSOVRKpW6tqmet6WjoyP/O2vWrNTc3Fzt5Ax7yqP2KJPaojxqS0cdnUPKMWr5WqQ/TaXyWb5GvfTSS+mJJ56odjIAAABgPqusskpacsklU10H3vPmzUuvvvpqamlpqeu7/wAAADSOcovnxRdfPI0cObK+A28AAACoZ6qQAQAAoEACbwAAACiQwJsFOuCAA9Ipp5wip6qovb09l8G2226bttlmm3TSSSeluXPnKpMquvLKK9Puu++eNt1007TXXnulW2+9VXnUgGnTpqUddtjBoJxV8txzz6XPfOYz+XfxoQ99KP3xj3+sVlKoEL0KDzvssPSb3/xGvlTZ73//+/TRj340/0Y+9rGPpZtvvrnaSRrWXn755XTkkUemzTffPO28887pggsuqHaS+K/7778/bbDBBnmg7UYh8KZfcZK+8cYbux6TRnVMmTIl3XTTTenHP/5x+sEPfpCuu+66dPLJJyuOKrnlllvS0Ucfnfbff/904YUX5kDvwAMPTH/961+VSRU9//zz6VOf+lT6xz/+0fUoEoZ2gJn4HUyYMCGde+65aZ999kmHH354evDBBxVDFcVN2q985SvpiiuucMO2yv785z+nL33pS2nffffN546ddtopHXzwwWnq1KnVTtqw9fnPfz7/e9555+Xj1be//e107bXXVjtZw157e3v68pe/nAfYHshjuuqFwJt+L2KjZvVtb3ubXKqiOODECSFqK1ZbbbX0zne+M9+Vve2225RLlVxyySXpgx/8YG6BsOyyy6a99947rbvuuukPf/iDMqmSuFCKi9i3vvWtyqCKQcXTTz+dvva1r+VyiN/I9ttvn375y18qkyqJ8ojzxaOPPppWWGEF5VAD544PfOADabvttsvnjqjxXn/99Z07qiR+F3fffXc+Zi2//PK51jvKw/VV9f3kJz9Jjaj/Mc8Z1o455pi0xx57pMcff7zaSRnWmpqa0jXXXJPGjRuX3//zn//MTdU222yzaidt2PrGN76RRo0aNV8T53IZUZ1HT0aLkLhReNVVVymCKrjvvvvSGmuskcaMGdM1bZ111hF4V9HMmTNzK4QIvqPpP9W/rur5uCHnjuo+d/n222/vOndHEB7HsbghQvU8/PDD+bzxox/9KFdsNBI13vTqsssuS0899VQ69NBD5VANBN7RdDOeY/+5z30u17LGxVT0L6Y6FltssTR69Oiu91HTHTeoohaD6oia1aipoHr+85//pDe/+c3dpsVzTaMPJdWx5pprpl133TU1Nzcrgho5d7S0tHS9j25jUevq3FEdcQO9HHRHbfeee+6ZJk2alDbccMMqpYiOjo7cxDy6ALzlLW9puAwReDOfGMTg+OOPz/1cKoMLqi/6df/lL3/Jg03EgF6zZs2qdpKGvbhDHn32vvOd76SVVlpp2OcHw7tbTNworNTzPfCae++9Nx111FHpuOOOS6uuuqpsqbLrr78+D3T3yiuv5K59VMeZZ56ZWltb802QRiTwHuY+/elP51qi8ivuNEUz2uiDFHf9GFqXX355t/K46KKLus2PWotoxhmBXvTBv/POOxVRwSrLIwbu6jnIWgyMExdOO+64o7IYAscee2y3MnniiSfke41YYoklcrPZStOnT8+13sDr/vSnP+UuAF//+tfTLrvsImtqQLQqXGaZZfJTGSIInzFjRrWTNOw8/vjjOfCOa6pGvWmrj/cw9/3vf7/b6L/xd/QfjrtN5aBvzpw5+QcQj4W5+uqrq5jaxhfNzaK5U1kE2S+++GJ6//vfn8ujXKMao8zH42HGjx9fxdQOD5WPQ6psrhkjBH/rW99KP/zhD3MLBIZG1BBV1kboV1871lprrfSrX/0qn0fKv5WHHnooTwdeE9dYUcER118bbbSRbKnyoJwnnnhiLpNyoBfXvNHas7JLAEPj97//fb5Z++EPfzi/L49mHl3JvvCFLzREf2+B9zAX/Y166lmLGhe6Mdpj7PQUKw72PZv3L7300mnttddO3/3ud9MJJ5yQD0QxQEv03Xv3u9+tSAoW/et7isHuvvrVr+bBP2KUeYbO2LFj84vas8kmm+Q+kxFQRK1RPCLp0ksvzQPkAK/dyI0Wa7/4xS+cv2tA3DSPMXNOPfXUdNBBB6Vnn302H79iXISeA6hSvP322y93oyyLwYSjRUg8eq9Rnsog8GaBgUYcfCIYVLNUPXFHNprYxoVtmDx5cjrttNOcGKokmkFFq4N4TnGleHxSPC8XhqNooTNlypQ8MM4ZZ5yRzyVHHHFEPl4Br5074rnqPbstxaMQ4xzP0IpjVFxLRWVG/BvHsDiP6+NdG5VP0/7bdSnij0YZc6qpFO1VoR+zZ8/OfV80u6m+aMIZzaGiPKienv1Yy+LEUPkoJYZenNKiqVqcqP1OqiduTDln1Jbos9pbqyqGThybervsjgoOLXmqq729XWVGjens7MzHrehW2Sh9vgXeAAAAUCDVZgAAAFAggTcAAAAUSOANAAAABRJ4AwAAQIEE3gAAAFAggTcAAAAUSOANANSsl19+Ob+G6/cD0BgE3gDUrRtvvDFtuOGGXa/JkyenrbbaKh111FHpH//4x0Kv91e/+lU68MADF/rzH/3oR9Nvf/vbhfrsk08+mdO/7bbbps033zx98pOfTFdfffUiS9uiyOeer29+85uFffd2222XnnrqqcLWX+vfD0BjGFntBADAwmpvb0+vvPJKuvXWW9OIEa/dS54xY0Y64YQT0ic+8Yl0zTXXpDFjxgx6vR/+8IfTzjvvvNDpevXVV9PcuXMX6nN77rln2n777dNPfvKTNHbs2HTHHXfkQHz27Nnpgx/84BtO28KIGxpXXXVV/ruzszO9973vTd/97nfTZpttlqe1tLQU9t3Tpk0rbN318P0ANAY13gDUvSWWWCK9+c1vzq+VVlop/d///V964YUX0p133pnn33XXXenjH/94rkH+1Kc+lf7yl790ffZnP/tZOvLII9NBBx2Uttlmm3Tbbbeliy66KH3xi1/sWubPf/5zDuTj83vvvXe6/fbbu33/lVdemYPiqG0//fTTu82bM2dOOvbYY9P73ve+9IEPfCB99atfzTcLehPfM2vWrLzM29/+9rT88svn9e6zzz45naEybb/4xS/Sl770pXTcccfl795hhx1yjXil2J7ytkfa4zvK+suXSqNHj+7K38jrMG7cuK5piy22WP5s3BT4zne+k7beeuuu9D7zzDPp85//fP6O3XffPae/UtTm77fffvkzsQ1RFnEDIuy44475309/+tN5u+6777606667pt/97nd5XnzmzDPPTA899FDeji233DJ95Stf6XbTo7/vL6/vnHPOyeuLz0delj/f8/sBYGEJvAFoOKNGjer694knnkj7779/DnojeIpgKgKpRx55JC8TNcnRLDyCrh/96Edp7bXXzsFyuaYzguwDDjggB86//OUvc7AXn//73/+e50cgG4H+vvvum4PABx54ID3++ONdafnBD36Q03Daaaflv6MpedTI9+Ytb3lLTk8ErRGAl33mM59JP//5z/PflWmLvy+55JLU1NSUzj777BxYf+Mb38iBaHjwwQdzWstpj8D44IMPzsHogvJlsObNm5e3febMmTkt0VQ+AtgI6Jdeeun8/REARx5ccMEF+TNTp07NtfmR7nPPPTd961vfSrfccks666yz8vxysHvyySfntEcLh0jf5ZdfnqZMmZJvlkReHnHEEXndsVy0crj00kvz5xb0/bG+v/3tb+m6665L3//+93PQHZ8tz+/5/QCwsATeADSUaGp+yimnpGWWWSZNnDgx10BH0+2o7VxxxRXTRz7ykfy+HMiGCMyiX/aaa66Za3IrRRAcNcl77bVXeutb35oDuag9/elPf9oVnEXwGs2/V1111dzfubW1tevzjz32WF7/KquskudHeqKWujfrrLNOOvzww3P6N9hgg/yd8XcEyeWa5p7e9KY35cA/avpj+fieu+++O8+LYDPSGrXBkfao3b3pppty3gwkXxZG3KSI74p1Rs10dAE4+uij87Rooh43EeIGRVhjjTXSzTffnG96LLvssrlJezRff+655/L88jaPHz++q8tABPixvbGdu+22W26O/6EPfSitv/76adKkSWnjjTdODz/8cF52Qd8fSqVSriVfbbXV8vy4YVDOv96+HwAWhj7eANS9CJjKQVQE3u985ztzzWYEwBGERe30H//4x67l29ra0lprrdX1Ppp09yU+H8FkpXXXXTcHdeX5MQBa2YQJE3JQWBbNxA855JBcc77FFlvkwG7TTTft8/viu+ImQATIf/rTn3INbNSWf+5zn8u11T2tsMIKqbm5uet9NPuOmtxy2qL5fKVIX3negvJlsKLmPWrty+I7nn322bTRRht1TYvAOWrq49/oGx5pjRYHkZZ4RT5Fk/P+RFBfFuuIPKh8H+sfyPeXxU2Lsrjx8vzzzy90HgBAbwTeANS9K664ItdsxiuC7eiTXBlMRh/iqA2uNHLk66fA/gYHi6C2ctkQ31MObmOwscrAt+f64qbAH/7wh/y64YYbcvPo6Lcdzar7svjii+da9HjF+qNZddxI6K25c7lZfaW4AVEOhHumbTD5MliRL5Xpie+IUc9PPPHE+ZaN74km+TGYXATOUcMf2zuQmuWe21weWK+nBX1/WeX+AgBFEHgDUPeiSXBfAWPUPkfNZwwCtjDi8+U+02XxPpouh7e97W3p0Ucf7ZoXNak9Hz8VzaijuXe8IgD/7Gc/m4455pj5Ar5oQh1BZeXjuSKojBrw6H8+ffr0QaV95ZVX7vOxam80XwYiviNq1KOpdm83CM4777xc23z++efnmwThwgsvzM3nh+L7AWCo6OMNQEOLYDcGz4qRx6MmOAYWi1reqEEe6OdjJOwYRC0+H8+0jhr2CIZD/HvxxRfnfsEdHR35MWAvvfRS1+djALMYnCsC8vj8X//61xy091bLGn3Jo9n1b37zm/Sf//wnT4t/o6l59FePIH8w9thjj/wYsPIo7NHfPJ5LHdvyRvNlIHbZZZc8WNz3vve9PNBZDAoX/amjpj1EHsQI7zGKeeRd9JePR8NFTXVZ1IDH5yqbhi+q7x+IN/L9AFAm8AagoUUT5qhdjqbdEbzG6N0xyFnPftt9icHJ/vd//ze/3v3ud+dHfcXI4dFfO0Qf6hgtO9YXg3vFSN2xXFk8Hiv6aq+33np58LTou91XcBvfFUF61ADHIGGxvhh4LAK/COjLtcID9Z73vCeP+B39wyNNEVjHgGQxiNkbzZeBiJrraCYfA6jF9sf3/vOf/+yq0Y+R4GOgt0022STPi+UOPPDArsHRysFz9JE/6aSTFvn3D8Qb+X4AKGsqlTuCAUCdiVrMGExtIM2l43QXj7qKwccqA9gYaCv6a0dz5P6mlT/fc9TzyvnxuRhlO5qERz/vylrtWF/Umsb8gYga4HikWHxfX+ntLZ3x3fG9lf3M+0t7X/nSn5dffjmvq+f2RVn01Uw8viPS1FuXgKjhjqbg5b7zsQ2VZRqfjZrnSGvchKicFzXmlf36Y9kQ27Og74/y6Lm+yPPoV1+ZV+Xv76u/PAAsiMAbAAAACqSpOQAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAgQTeAAAAUCCBNwAAABRI4A0AAAAFEngDAABAKs7/BzXxh7HAhgYxAAAAAElFTkSuQmCC", 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", 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" ] @@ -1282,16 +1258,16 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.520867Z", - "iopub.status.busy": "2026-08-10T22:40:30.520805Z", - "iopub.status.idle": "2026-08-10T22:40:30.568881Z", - "shell.execute_reply": "2026-08-10T22:40:30.568503Z" + "iopub.execute_input": "2026-08-18T20:30:20.453157Z", + "iopub.status.busy": "2026-08-18T20:30:20.453084Z", + "iopub.status.idle": "2026-08-18T20:30:20.495716Z", + "shell.execute_reply": "2026-08-18T20:30:20.495418Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1342,10 +1318,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.569795Z", - "iopub.status.busy": "2026-08-10T22:40:30.569722Z", - "iopub.status.idle": "2026-08-10T22:40:30.574726Z", - "shell.execute_reply": "2026-08-10T22:40:30.574395Z" + "iopub.execute_input": "2026-08-18T20:30:20.496834Z", + "iopub.status.busy": "2026-08-18T20:30:20.496766Z", + "iopub.status.idle": "2026-08-18T20:30:20.501910Z", + "shell.execute_reply": "2026-08-18T20:30:20.501540Z" } }, "outputs": [ @@ -1381,10 +1357,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.575623Z", - "iopub.status.busy": "2026-08-10T22:40:30.575566Z", - "iopub.status.idle": "2026-08-10T22:40:30.578582Z", - "shell.execute_reply": "2026-08-10T22:40:30.578250Z" + "iopub.execute_input": "2026-08-18T20:30:20.502803Z", + "iopub.status.busy": "2026-08-18T20:30:20.502736Z", + "iopub.status.idle": "2026-08-18T20:30:20.505820Z", + "shell.execute_reply": "2026-08-18T20:30:20.505567Z" } }, "outputs": [ @@ -1445,10 +1421,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.579366Z", - "iopub.status.busy": "2026-08-10T22:40:30.579311Z", - "iopub.status.idle": "2026-08-10T22:40:30.585047Z", - "shell.execute_reply": "2026-08-10T22:40:30.584662Z" + "iopub.execute_input": "2026-08-18T20:30:20.506819Z", + "iopub.status.busy": "2026-08-18T20:30:20.506758Z", + "iopub.status.idle": "2026-08-18T20:30:20.512379Z", + "shell.execute_reply": "2026-08-18T20:30:20.511979Z" } }, "outputs": [ @@ -1549,10 +1525,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.586105Z", - "iopub.status.busy": "2026-08-10T22:40:30.586033Z", - "iopub.status.idle": "2026-08-10T22:40:30.590828Z", - "shell.execute_reply": "2026-08-10T22:40:30.590480Z" + "iopub.execute_input": "2026-08-18T20:30:20.513262Z", + "iopub.status.busy": "2026-08-18T20:30:20.513208Z", + "iopub.status.idle": "2026-08-18T20:30:20.518004Z", + "shell.execute_reply": "2026-08-18T20:30:20.517629Z" } }, "outputs": [ @@ -1604,10 +1580,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.591660Z", - "iopub.status.busy": "2026-08-10T22:40:30.591603Z", - "iopub.status.idle": "2026-08-10T22:40:30.595842Z", - "shell.execute_reply": "2026-08-10T22:40:30.595474Z" + "iopub.execute_input": "2026-08-18T20:30:20.518869Z", + "iopub.status.busy": "2026-08-18T20:30:20.518815Z", + "iopub.status.idle": "2026-08-18T20:30:20.523225Z", + "shell.execute_reply": "2026-08-18T20:30:20.522889Z" } }, "outputs": [ @@ -1651,10 +1627,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.596623Z", - "iopub.status.busy": "2026-08-10T22:40:30.596569Z", - "iopub.status.idle": "2026-08-10T22:40:30.607577Z", - "shell.execute_reply": "2026-08-10T22:40:30.607304Z" + "iopub.execute_input": "2026-08-18T20:30:20.524154Z", + "iopub.status.busy": "2026-08-18T20:30:20.524088Z", + "iopub.status.idle": "2026-08-18T20:30:20.534972Z", + "shell.execute_reply": "2026-08-18T20:30:20.534675Z" } }, "outputs": [ @@ -1662,7 +1638,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "With covariates: ATT = 3.4595 (SE: 0.1135)\n" + "With covariates: ATT = 3.4780 (SE: 0.1257)\n" ] } ], @@ -1706,10 +1682,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.608372Z", - "iopub.status.busy": "2026-08-10T22:40:30.608319Z", - "iopub.status.idle": "2026-08-10T22:40:30.612675Z", - "shell.execute_reply": "2026-08-10T22:40:30.612333Z" + "iopub.execute_input": "2026-08-18T20:30:20.535892Z", + "iopub.status.busy": "2026-08-18T20:30:20.535840Z", + "iopub.status.idle": "2026-08-18T20:30:20.541173Z", + "shell.execute_reply": "2026-08-18T20:30:20.540876Z" } }, "outputs": [ @@ -1772,9 +1748,9 @@ "name": "stderr", "output_type": "stream", "text": [ - ".py:18: FutureWarning: MultiPeriodDiD is deprecated and will be removed in 4.0; use TwoWayFixedEffects().fit(..., event_study=True) instead - spec='pooled' reproduces the MultiPeriodDiD design; the default spec='within' adds unit fixed effects. The EventStudy alias is deprecated with it.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51857/1162828311.py:18: FutureWarning: MultiPeriodDiD is deprecated and will be removed in 4.0; use TwoWayFixedEffects().fit(..., event_study=True) instead - spec='pooled' reproduces the MultiPeriodDiD design; the default spec='within' adds unit fixed effects. The EventStudy alias is deprecated with it.\n", " mp_did = MultiPeriodDiD()\n", - ".py:19: FutureWarning: The default reference_period has changed from the first pre-period (0) to the last pre-period (3) to match the standard e=-1 convention (as used by fixest, did, etc.). To silence this warning, pass reference_period=3 explicitly.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51857/1162828311.py:19: FutureWarning: The default reference_period has changed from the first pre-period (0) to the last pre-period (3) to match the standard e=-1 convention (as used by fixest, did, etc.). To silence this warning, pass reference_period=3 explicitly.\n", " results_mp = mp_did.fit(\n" ] } @@ -1814,10 +1790,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.613529Z", - "iopub.status.busy": "2026-08-10T22:40:30.613464Z", - "iopub.status.idle": "2026-08-10T22:40:30.615253Z", - "shell.execute_reply": "2026-08-10T22:40:30.614927Z" + "iopub.execute_input": "2026-08-18T20:30:20.542050Z", + "iopub.status.busy": "2026-08-18T20:30:20.541998Z", + "iopub.status.idle": "2026-08-18T20:30:20.543797Z", + "shell.execute_reply": "2026-08-18T20:30:20.543438Z" } }, "outputs": [ @@ -1869,10 +1845,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.616070Z", - "iopub.status.busy": "2026-08-10T22:40:30.616015Z", - "iopub.status.idle": "2026-08-10T22:40:30.625736Z", - "shell.execute_reply": "2026-08-10T22:40:30.625383Z" + "iopub.execute_input": "2026-08-18T20:30:20.544692Z", + "iopub.status.busy": "2026-08-18T20:30:20.544615Z", + "iopub.status.idle": "2026-08-18T20:30:20.554412Z", + "shell.execute_reply": "2026-08-18T20:30:20.554066Z" } }, "outputs": [ @@ -1947,10 +1923,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.626603Z", - "iopub.status.busy": "2026-08-10T22:40:30.626546Z", - "iopub.status.idle": "2026-08-10T22:40:30.628915Z", - "shell.execute_reply": "2026-08-10T22:40:30.628592Z" + "iopub.execute_input": "2026-08-18T20:30:20.555272Z", + "iopub.status.busy": "2026-08-18T20:30:20.555212Z", + "iopub.status.idle": "2026-08-18T20:30:20.557501Z", + "shell.execute_reply": "2026-08-18T20:30:20.557191Z" } }, "outputs": [ @@ -2041,10 +2017,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:30.629785Z", - "iopub.status.busy": "2026-08-10T22:40:30.629727Z", - "iopub.status.idle": "2026-08-10T22:40:30.635992Z", - "shell.execute_reply": "2026-08-10T22:40:30.635696Z" + "iopub.execute_input": "2026-08-18T20:30:20.558555Z", + "iopub.status.busy": "2026-08-18T20:30:20.558502Z", + "iopub.status.idle": "2026-08-18T20:30:20.565205Z", + "shell.execute_reply": "2026-08-18T20:30:20.564885Z" } }, "outputs": [ diff --git a/docs/tutorials/09_real_world_examples.ipynb b/docs/tutorials/09_real_world_examples.ipynb index 02755988b..3e152707a 100644 --- a/docs/tutorials/09_real_world_examples.ipynb +++ b/docs/tutorials/09_real_world_examples.ipynb @@ -24,10 +24,10 @@ "id": "cell-1", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:32.894146Z", - "iopub.status.busy": "2026-08-10T22:40:32.893882Z", - "iopub.status.idle": "2026-08-10T22:40:33.605559Z", - "shell.execute_reply": "2026-08-10T22:40:33.605066Z" + "iopub.execute_input": "2026-08-18T20:30:54.873413Z", + "iopub.status.busy": "2026-08-18T20:30:54.873083Z", + "iopub.status.idle": "2026-08-18T20:30:55.566559Z", + "shell.execute_reply": "2026-08-18T20:30:55.566106Z" } }, "outputs": [], @@ -66,10 +66,10 @@ "id": "cell-2", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.606760Z", - "iopub.status.busy": "2026-08-10T22:40:33.606668Z", - "iopub.status.idle": "2026-08-10T22:40:33.608858Z", - "shell.execute_reply": "2026-08-10T22:40:33.608469Z" + "iopub.execute_input": "2026-08-18T20:30:55.567849Z", + "iopub.status.busy": "2026-08-18T20:30:55.567747Z", + "iopub.status.idle": "2026-08-18T20:30:55.569852Z", + "shell.execute_reply": "2026-08-18T20:30:55.569538Z" } }, "outputs": [ @@ -126,10 +126,10 @@ "id": "cell-4", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.609649Z", - "iopub.status.busy": "2026-08-10T22:40:33.609596Z", - "iopub.status.idle": "2026-08-10T22:40:33.620516Z", - "shell.execute_reply": "2026-08-10T22:40:33.620154Z" + "iopub.execute_input": "2026-08-18T20:30:55.570895Z", + "iopub.status.busy": "2026-08-18T20:30:55.570840Z", + "iopub.status.idle": "2026-08-18T20:30:55.582137Z", + "shell.execute_reply": "2026-08-18T20:30:55.581767Z" } }, "outputs": [ @@ -287,10 +287,10 @@ "id": "cell-5", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.621423Z", - "iopub.status.busy": "2026-08-10T22:40:33.621361Z", - "iopub.status.idle": "2026-08-10T22:40:33.626695Z", - "shell.execute_reply": "2026-08-10T22:40:33.626381Z" + "iopub.execute_input": "2026-08-18T20:30:55.583064Z", + "iopub.status.busy": "2026-08-18T20:30:55.583013Z", + "iopub.status.idle": "2026-08-18T20:30:55.588529Z", + "shell.execute_reply": "2026-08-18T20:30:55.588260Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "id": "cell-7", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.627538Z", - "iopub.status.busy": "2026-08-10T22:40:33.627480Z", - "iopub.status.idle": "2026-08-10T22:40:33.633789Z", - "shell.execute_reply": "2026-08-10T22:40:33.633463Z" + "iopub.execute_input": "2026-08-18T20:30:55.589522Z", + "iopub.status.busy": "2026-08-18T20:30:55.589468Z", + "iopub.status.idle": "2026-08-18T20:30:55.595956Z", + "shell.execute_reply": "2026-08-18T20:30:55.595664Z" } }, "outputs": [ @@ -578,10 +578,10 @@ "id": "cell-9", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.634594Z", - "iopub.status.busy": "2026-08-10T22:40:33.634539Z", - "iopub.status.idle": "2026-08-10T22:40:33.637499Z", - "shell.execute_reply": "2026-08-10T22:40:33.637129Z" + "iopub.execute_input": "2026-08-18T20:30:55.597020Z", + "iopub.status.busy": "2026-08-18T20:30:55.596967Z", + "iopub.status.idle": "2026-08-18T20:30:55.600073Z", + "shell.execute_reply": "2026-08-18T20:30:55.599757Z" } }, "outputs": [ @@ -637,10 +637,10 @@ "id": "cell-10", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.638320Z", - "iopub.status.busy": "2026-08-10T22:40:33.638261Z", - "iopub.status.idle": "2026-08-10T22:40:33.641923Z", - "shell.execute_reply": "2026-08-10T22:40:33.641577Z" + "iopub.execute_input": "2026-08-18T20:30:55.601076Z", + "iopub.status.busy": "2026-08-18T20:30:55.601021Z", + "iopub.status.idle": "2026-08-18T20:30:55.604596Z", + "shell.execute_reply": "2026-08-18T20:30:55.604322Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "id": "cell-11", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.642744Z", - "iopub.status.busy": "2026-08-10T22:40:33.642677Z", - "iopub.status.idle": "2026-08-10T22:40:33.645875Z", - "shell.execute_reply": "2026-08-10T22:40:33.645524Z" + "iopub.execute_input": "2026-08-18T20:30:55.605580Z", + "iopub.status.busy": "2026-08-18T20:30:55.605514Z", + "iopub.status.idle": "2026-08-18T20:30:55.608877Z", + "shell.execute_reply": "2026-08-18T20:30:55.608582Z" } }, "outputs": [ @@ -765,16 +765,16 @@ "id": "cell-13", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.646719Z", - "iopub.status.busy": "2026-08-10T22:40:33.646661Z", - "iopub.status.idle": "2026-08-10T22:40:33.764535Z", - "shell.execute_reply": "2026-08-10T22:40:33.764191Z" + "iopub.execute_input": "2026-08-18T20:30:55.609864Z", + "iopub.status.busy": "2026-08-18T20:30:55.609811Z", + "iopub.status.idle": "2026-08-18T20:30:55.700007Z", + "shell.execute_reply": "2026-08-18T20:30:55.699661Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -856,10 +856,10 @@ "id": "cell-15", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.765501Z", - "iopub.status.busy": "2026-08-10T22:40:33.765425Z", - "iopub.status.idle": "2026-08-10T22:40:33.777035Z", - "shell.execute_reply": "2026-08-10T22:40:33.776756Z" + "iopub.execute_input": "2026-08-18T20:30:55.701019Z", + "iopub.status.busy": "2026-08-18T20:30:55.700946Z", + "iopub.status.idle": "2026-08-18T20:30:55.713611Z", + "shell.execute_reply": "2026-08-18T20:30:55.713329Z" } }, "outputs": [ @@ -1010,10 +1010,10 @@ "id": "cell-16", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.777880Z", - "iopub.status.busy": "2026-08-10T22:40:33.777825Z", - "iopub.status.idle": "2026-08-10T22:40:33.781238Z", - "shell.execute_reply": "2026-08-10T22:40:33.780851Z" + "iopub.execute_input": "2026-08-18T20:30:55.714631Z", + "iopub.status.busy": "2026-08-18T20:30:55.714575Z", + "iopub.status.idle": "2026-08-18T20:30:55.718031Z", + "shell.execute_reply": "2026-08-18T20:30:55.717677Z" } }, "outputs": [ @@ -1066,10 +1066,10 @@ "id": "cell-18", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.782017Z", - "iopub.status.busy": "2026-08-10T22:40:33.781956Z", - "iopub.status.idle": "2026-08-10T22:40:33.787059Z", - "shell.execute_reply": "2026-08-10T22:40:33.786712Z" + "iopub.execute_input": "2026-08-18T20:30:55.718942Z", + "iopub.status.busy": "2026-08-18T20:30:55.718890Z", + "iopub.status.idle": "2026-08-18T20:30:55.723599Z", + "shell.execute_reply": "2026-08-18T20:30:55.723207Z" } }, "outputs": [ @@ -1089,14 +1089,14 @@ "name": "stderr", "output_type": "stream", "text": [ - ".py:7: FutureWarning: TwoWayFixedEffects.fit(time=) is deprecated and will be removed in 4.0; use post= instead. From 4.0, time= means the event-study calendar column only.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51890/1694913614.py:7: FutureWarning: TwoWayFixedEffects.fit(time=) is deprecated and will be removed in 4.0; use post= instead. From 4.0, time= means the event-study calendar column only.\n", " results_twfe = twfe.fit(\n", - ".py:7: UserWarning: Staggered treatment timing detected: 5 treatment cohorts start treatment at different times. TWFE can be biased when treatment effects are heterogeneous across time. Consider using:\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51890/1694913614.py:7: UserWarning: Staggered treatment timing detected: 5 treatment cohorts start treatment at different times. TWFE can be biased when treatment effects are heterogeneous across time. Consider using:\n", " - CallawaySantAnna estimator for robust estimates\n", " - TwoWayFixedEffects.decompose() to diagnose the decomposition\n", " - BaconDecomposition().fit(...) to see weight on 'forbidden' comparisons\n", " results_twfe = twfe.fit(\n", - ".py:7: UserWarning: The 'year' column has 11 unique values. TwoWayFixedEffects expects a binary (0/1) post indicator. Multi-period time values produce 'treated * period_number' instead of 'treated * post_indicator', which may not estimate the standard DiD ATT. Consider creating a binary post column: df['post'] = (df['year'] >= cutoff).astype(int)\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51890/1694913614.py:7: UserWarning: The 'year' column has 11 unique values. TwoWayFixedEffects expects a binary (0/1) post indicator. Multi-period time values produce 'treated * period_number' instead of 'treated * post_indicator', which may not estimate the standard DiD ATT. Consider creating a binary post column: df['post'] = (df['year'] >= cutoff).astype(int)\n", " results_twfe = twfe.fit(\n" ] } @@ -1129,10 +1129,10 @@ "id": "cell-19", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.787892Z", - "iopub.status.busy": "2026-08-10T22:40:33.787841Z", - "iopub.status.idle": "2026-08-10T22:40:33.815923Z", - "shell.execute_reply": "2026-08-10T22:40:33.815641Z" + "iopub.execute_input": "2026-08-18T20:30:55.724518Z", + "iopub.status.busy": "2026-08-18T20:30:55.724466Z", + "iopub.status.idle": "2026-08-18T20:30:55.742463Z", + "shell.execute_reply": "2026-08-18T20:30:55.742173Z" } }, "outputs": [ @@ -1198,16 +1198,16 @@ "id": "cell-20", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.816988Z", - "iopub.status.busy": "2026-08-10T22:40:33.816938Z", - "iopub.status.idle": "2026-08-10T22:40:33.941696Z", - "shell.execute_reply": "2026-08-10T22:40:33.941304Z" + "iopub.execute_input": "2026-08-18T20:30:55.743598Z", + "iopub.status.busy": "2026-08-18T20:30:55.743541Z", + "iopub.status.idle": "2026-08-18T20:30:55.839883Z", + "shell.execute_reply": "2026-08-18T20:30:55.839486Z" } }, "outputs": [ { "data": { - "image/png": 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Jgghbb721u5ilZT8/D4KBqB2wVahi2Y9NhyxgR3bd2S3fGnN/Kn6O8VkQ0xbBoOXss0/Fcn1PBtq2YTTndvXrmP/617+6Orc77bRTRZ908eLFttlmm7nRXaLJdi+//HJfXg8AAACorTpNkft///d/tu+++7os2xUrVrhJwkaMGOGCuHLkkUfaiSeeaDk5OXGfX1hYuMFjubm57v+aMsn0PGX9PvHEE642mTrciaxdu9ZKS0stU76kqN0kXpkK0K6phnO24dpVmV/KCvP7s8D7VC5Y4982W/w+sMMLtOj//Px8SzWcr1VkZ5f/X1BA26ao5nzO+jXqoGvXrq5+7bRp0+zHH3908zT079/f9TM977zzjlsPAAAASOnA7aJFi1xGrDq1v/76q7tPgVovWKvatrr16NHD1QarjkoclJSUxO2Et2zZstrnHnLIIe7/IUOG2JlnnulKKGgIWzytW7eucXvpwgt6tGvXrtl9QWtItCttm27S9Zz19lX/a99TTbq2azqgbWlXv3kB67rSpLgKzn7zzTcVtWu90V5lZWX2xRdfuGUlJRC0BQAAQFoEbl9//XU3PFdlC7zhY/GcfPLJbtKx6iiwq0zYZcuWuWxdUYmErKysuEFYlVZ4+eWXXaDW+0I9aNAg19lWSYZEgVutm0lfwL3jyaRjSgW0K22bTgEwlYnRZ7HfAcZwJGrfLywPYGzeM2ihYP23HY5GbEpB+YSVsVL1M4zPgnLRcNjCP/7olkODB1sgVF5Cg7ZNPc31nK3v8ao/+emnn7r/q8veVWA30egxAAAAIKUCtwrIxluui379+rng7auvvupKKogmPBs5cmTcCccULL7//vtt8803t513Lq+X+Pbbb1vbtm3dkDYAaA50sUrDeRVo2HjjjX2dLCcSNftqTnngdnCPoNU/VFceuP1k9fc+bAmNKhKx0smT3WJo4EAzHwK3QCr5+uuvK/0PAAAAZFSNW1HgYM6cOa42rYaXqWZhbYa/XnHFFW7Ch5kzZ7qJzT7++GNXt9ajCchUw/Hwww93273sssvskksusQMOOMBWr15t77//vt1yyy1xA70AkKk6dOjgPjMBAP7QhGTz5s1zCQS6IFbbPi0AAADQUOqUrqWJGnbbbTc7+OCD3QRh8uyzz9oFF1yQ9Ey/u+66q7300ktu5l7VEFMpBpU/SOSYY46xxx9/3Pr06WPbbbedvfLKKzZ27Ni67D4ApCXVGB82bJgNHjzYLQMA6m7dunUuiUCjuU444YSKCW333ntvmzp1Kk0LAACA9Mu41SRlyn699tprbfbs2VZUVOTu1yRhRxxxhKsZtsMOOyS1LZU5SFTqYM8999zgvoEDB7obAAAAUB+33XabC96+9957LljrOemkk+ymm26yZ555hgYGAABAemXcfvbZZy5bdp999qk0YUOnTp1s9913t2+//dbvfQQAAAB8pbJbV199tfXs2bPS/ePGjbPvv/8+6VFkAAAAQMoEbr0M23jWrl3L8F0AaCDhcNi++uormzZtmlsGANRdoj6tsnADgYC7AQAAAGkVuN1pp53s7bffdjcvcKCJch599FF7+eWX3eMAgIahgAKTkwFA/WmkmEp/LV++vOK+GTNm2Pnnn2+77LILTQwAAID0q3Hbu3dvu/nmm+2aa65xHd28vDx7+OGHrVWrVm64WXUTjAEA6k6znW+11VZWUFDglv0UCprtOyxUseyHrEDQDus6xi3f6s8m0RhCIcvx6swzCR4y2J///Ge7/PLLbcyYMa4swqhRo6ywsNC23357+8tf/tLUuwcAAAAkH7jVRGTZ2dnWp08fN3HYjjvuaN9995399ttv1rlzZ9t8882tXbt2NCkANBAN223fvn2DDOENBgK2UXu/txm0XnldfN0mGl4gGLRQ9+40NTKW5msYPny4tW7d2u666y6bN2+e/fzzz1ZaWmobb7wxSQgAAABIv8CtJnC49dZbXcatMhN0U2bCiBEjGnYPAQAAAJ+ceeaZFolEXB92hx12cLexY8fSvgAAAEjfwO3JJ5/ssmy//vprNzmOyiKsWrXKhg0b5jq8emyLLbbwffguAKCchvKqRI1KJbRt29bXrNtwJGozfyufQX3gRgELBeu/7XA0Yt+tnevD3qExRcNhC//0k1sObbaZBSiXgAzz0Ucf2TfffOP6s2+88Ybddttt1rFjR9efVWLC6NGj3c8AAABA2gRuFSAYOHCgux177LHuvrlz57pA7ieffGL33nuvtWnTxi6++GI74ogjGnKfAaBZUoaYStQUFxe7sjV+XiiLRM0++7l8wsnNumdZebXb+gduP1j1rQ9bQqOKRKz0yy/dYqh/f+rcIuPowteuu+7qbqK6tlOmTLEvv/zSlU5QX3bIkCH27LPPujJhAAAAQNpMThZLwQNl3arObTgcdp3brKx6bRIAUA3VEleQAQDgD/VhNZJh6dKllp+f7y6K5eTk0LwAAABocrWKsqozq+zaSZMmuZt+1uQOqgum0gmDBg3yfcIcAEC5UChkW2+9tfvs1TIAoG5lZ3744QfXl1XZhGnTplm3bt1cqYRrr73Wtt9+e5eVCwAAAKRN4Hb8+PEuONu3b1/Xsb3hhhts5MiR1qJFi4bdQwAAAMAnKpGgC2Dqx+699952/fXX2yabbEL7AgAAIOUkXSCxrKzMDR3r1KmTm7ChQ4cOlpub27B7BwAAAPiotLTUzcug/qz6terTAgAAAGmdcXvMMcfYnnvuaR9//LEbVvaf//zHlUXwZuDV/127dm3YvQWAZl6HURPorF+/3g3lpaY4ANSeVx5B/z/yyCP2pz/9yQYPHuz6szvuuKMNGzaMz1cAAACkX43bzp0728EHH+xumt18+vTprj6YOr1//vOfbeDAgXbOOee4AC8AwH+aQEcTQwIA6kY1wjVHg27nn3++rVy50iUmqE970kknuYnJdHHsjjvuIIALAACA9AncxlLZhE033dTWrVtnJSUlNmfOHJs5c6b7HwDgP33uDh061AVvteynUNBsjy1CFct+yAoE7cAu27vlW/3ZJBpDKGQ5u+1WsQxkOpVM0Ger+rPz5s2zb7/91gVylaQAAAAApE3gdunSpfbNN9+42+TJk23GjBmuNtioUaPcxA6jR492s/ICAPyn8jT6zFWJBC37KRgIWJ9Ofm8zaBu36O7rNtHwAsGghXr1oqmR0TVuf/jhh0p92jVr1rjgrUolXHrppa5cQnZ2dlPvKgAAAJq5pAO3qmn7t7/9zVq1auVm4T3ooIPs5ptvts0226xh9xAAAADwiRINFKjVyDGVRLjhhhtc37Z169a0MQAAANIzcLvtttvaU089xYQNANBEotGoq8WoUglt27b1Nes2HInanKVRt7xJ14CFgvXfdjgasZmFv/qwd2hM0XDYwnPnuuXQxhtbgHIJyDBXX321jRgxgkl1AQAAkDmB280337xh9wQAUC3VW1TtRU1O1qtXL1/r3EaiZh/NDLvlfl2yzI/Kpgrcvr1isg9bQqOKRKz000/dYqhvX+rcIuPst99+Tb0LAAAAQMNOTgYAaHwayqsZ0QEAAAAAQGYjcAsAaUIBW5Wtyc/PJ3gLAAAAAECG82+cLQAAAAAAAADAFwRuAQAAAAAAACDFELgFgDQRDodt6tSp9sMPP7hlAAAAAACQuahxCwBpZPXq1VZcXNzUuwEAAAAAABoYgVsASBPBYNCGDBlia9eudct+CgXNdh0cqlj2Q1YgaPt0HuGWb/Vnk2gMoZDl7LRTxTIAZJJWoTzbs+M21ievq60qK7C3V0y230pW+rb9fnndbLeOW1vrYJ69v2SyfVsyv9r1WwZz7fSe+9ms9Qvt1eVfuPtyAlk2tuNw699iI4ua2ffr5tkHq6a5ZRnZdqANb7OZZQVC9sO6+e6xSMWjAAAgk1AqAQDSRCAQsK5du1qnTp3csp+CgYBt0jXoblr2Z5tBG9Cyl7shfQSCQQv16+duWgaATJEbzLYr+x1jW7bexN5dOcW6Zre3P/c7yjpnt/Vl+xvldLRL+h5p4WjYfl6/0P646WE2ou3AhOv3zO1kF/c93Ia07mvdczpU3H9Kz33sgC6jbPq6efblmhl2WNcd3c0L2p7ac19bWrLapq2dY+O67WL7dx7ly/4DAIDUwzcyAAAAABlPWaqdc9rZ6yu+tO/WzbPXV3xlLUO5tnmrfr5sf9cOwywnmGUTl31ury7/0gpKC13mbDzbtxtsf9n4OGsdarnBY+OXfGjXz33KZdJ+uWam/Vy4yHbtsJUFLOCCzvLfZZ/apNXTbXHJStu6TX9f9h8AAKQeArcAkCai0ajl5+fbmjVr3LKfItGozVkacTct+7PNiM0qXOBuSB/RSMTC8+a5m5YBIFN0y2nv/l9Vutb9P7hVH/f/ouIVvmx/4xYbWTgasZVlBe7nZcWrrW+Lrha0DUeytAzm2Xdr59p9C17e4LHlpWtsYfFyt6xgbffcDi4gnBfMthWla9z9CkDnBrKtXVYrtz4AAMhM1LgFgDQRiURsypQpbnKyHj16+FrnNhwxe//HsFs+vlOWBX0obVoWjdjry7+q/4bQuMJhK/noI7eYd/TRKq7MOwAgI6gmrEQtatdscrz1yO1kLy39xH5avzDu+uf2OshahHI3uH9ywU/2zsopG9zfLqullURKK34uipS412wVamEF4cJK6368+jt7d9UU65Ldrtp9PrjLaOuU3dZWlhbY+kiJvbb8SxvYspdd2neclURLrTRSZk8tfi/pNgAAAOmFwC0ApJEWLVr4Xt8WAIDm5oWlH9u+nUfagV22tyUlq+3rglkbrPPK8i8sFNjw4tXqsnVxt6l1FRSON1lnVcXR/wV4E9mp/VC3j/LR6unu/2FtNnGZvW+s+MqWlebbsd13s306jbCnlrxf4/YAAED6IXALAGkiFArZdttt58olaBkAANSeJuHUxF5z1y+2mzc7xU0EFi9wu3/n7WqVcVsUKbV2WTkbZPgqU7a2hrb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", 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", "text/plain": [ "
" ] @@ -1258,10 +1258,10 @@ "id": "cell-22", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.942587Z", - "iopub.status.busy": "2026-08-10T22:40:33.942529Z", - "iopub.status.idle": "2026-08-10T22:40:33.953188Z", - "shell.execute_reply": "2026-08-10T22:40:33.952900Z" + "iopub.execute_input": "2026-08-18T20:30:55.840933Z", + "iopub.status.busy": "2026-08-18T20:30:55.840859Z", + "iopub.status.idle": "2026-08-18T20:30:55.848624Z", + "shell.execute_reply": "2026-08-18T20:30:55.848257Z" } }, "outputs": [ @@ -1286,57 +1286,15 @@ "-------------------------------------------------------------------------------------\n", "Parameter Estimate Std. Err. t-stat P>|t| Sig.\n", "-------------------------------------------------------------------------------------\n", - "ATT 0.4725 0.2071 2.281 0.0201 *\n", + "ATT 0.4725 0.1946 2.428 0.0050 **\n", "-------------------------------------------------------------------------------------\n", "\n", - "95% Confidence Interval: [0.0702, 0.8046]\n", - "CV (SE/abs(ATT)): 0.4384\n", - "\n", - "-------------------------------------------------------------------------------------\n", - " Event Study (Dynamic) Effects \n", - "-------------------------------------------------------------------------------------\n", - "Rel. Period Estimate Std. Err. t-stat P>|t| Sig.\n", - "-------------------------------------------------------------------------------------\n", - "-8 1.6104 0.1450 11.104 0.0050 **\n", - "-7 -0.7193 0.6174 -1.165 0.3116 \n", - "-6 0.6331 0.1778 3.562 0.0050 **\n", - "-5 0.0078 0.2481 0.032 0.8945 \n", - "-4 -0.3901 0.1963 -1.987 0.0402 *\n", - "-3 0.3038 0.1509 2.013 0.0101 *\n", - "-2 -0.1471 0.1801 -0.817 0.4523 \n", - "-1 -0.3231 0.2424 -1.333 0.2211 \n", - "0 0.4655 0.1637 2.843 0.0050 **\n", - "1 0.7781 0.2499 3.113 0.0050 **\n", - "2 0.4840 0.2533 1.911 0.0402 *\n", - "3 0.3673 0.2951 1.244 0.2714 \n", - "4 0.1541 0.3286 0.469 0.7236 \n", - "5 0.3210 0.1829 1.755 0.0704 .\n", - "-------------------------------------------------------------------------------------\n", - "Simult. CI: critical value = 2.6290 (sup-t bootstrap, 95% family-wise)\n", - "\n", - "-------------------------------------------------------------------------------------\n", - " Effects by Treatment Cohort \n", - "-------------------------------------------------------------------------------------\n", - "Cohort Estimate Std. Err. t-stat P>|t| Sig.\n", - "-------------------------------------------------------------------------------------\n", - "2005 0.4412 0.0990 4.455 0.0050 **\n", - "2006 0.4991 0.2787 1.791 0.0804 .\n", - "2007 0.4179 0.2383 1.754 0.1005 \n", - "2008 0.5252 0.2110 2.490 0.0101 *\n", - "2009 -0.0218 0.1104 -0.198 0.9950 \n", - "-------------------------------------------------------------------------------------\n", + "95% Confidence Interval: [0.1029, 0.8253]\n", + "CV (SE/abs(ATT)): 0.4119\n", "\n", "Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1\n", "=====================================================================================\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ".py:8: FutureWarning: CallawaySantAnna.fit(aggregate=) is deprecated and will be removed in 4.0. Fit once, then aggregate as a post-fit step: results = CallawaySantAnna().fit(...); results.aggregate('event_study') / .aggregate('group') / .aggregate('simple'). balance_e moves onto aggregate() alongside it: results.aggregate('event_study', balance_e=2).\n", - " results_cs = cs.fit(\n" - ] } ], "source": [ @@ -1353,11 +1311,6 @@ " unit='state',\n", " time='year',\n", " first_treat='first_treat',\n", - " # Fit-time aggregation is the documented exception for BOOTSTRAPPED fits\n", - " # until 4.0: post-fit results.aggregate('event_study'/'group') raises here\n", - " # because the percentile draws are not retained (aggregate('simple')\n", - " # relays the stored bootstrap inference). Expect a FutureWarning.\n", - " aggregate='all'\n", ")\n", "\n", "print(results_cs.summary())" @@ -1369,10 +1322,10 @@ "id": "cell-23", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.954080Z", - "iopub.status.busy": "2026-08-10T22:40:33.954025Z", - "iopub.status.idle": "2026-08-10T22:40:33.956256Z", - "shell.execute_reply": "2026-08-10T22:40:33.955902Z" + "iopub.execute_input": "2026-08-18T20:30:55.849525Z", + "iopub.status.busy": "2026-08-18T20:30:55.849466Z", + "iopub.status.idle": "2026-08-18T20:30:55.853616Z", + "shell.execute_reply": "2026-08-18T20:30:55.853253Z" } }, "outputs": [ @@ -1383,15 +1336,15 @@ "Aggregated Results\n", "============================================================\n", "\n", - "Overall ATT: 0.4725 (SE: 0.2071)\n", - "95% CI: [0.0702, 0.8046]\n", + "Overall ATT: 0.4725 (SE: 0.1946)\n", + "95% CI: [0.1029, 0.8253]\n", "\n", "Effects by Adoption Cohort:\n", - " Cohort 2005: 0.4412 (SE: 0.0990)\n", - " Cohort 2006: 0.4991 (SE: 0.2787)\n", - " Cohort 2007: 0.4179 (SE: 0.2383)\n", - " Cohort 2008: 0.5252 (SE: 0.2110)\n", - " Cohort 2009: -0.0218 (SE: 0.1104)\n" + " Cohort 2005: 0.4412 (SE: 0.1094)\n", + " Cohort 2006: 0.4991 (SE: 0.2602)\n", + " Cohort 2007: 0.4179 (SE: 0.2365)\n", + " Cohort 2008: 0.5252 (SE: 0.1873)\n", + " Cohort 2009: -0.0218 (SE: 0.1044)\n" ] } ], @@ -1404,12 +1357,12 @@ "print(f\"\\nOverall ATT: {results_cs.overall_att:.4f} (SE: {results_cs.overall_se:.4f})\")\n", "print(f\"95% CI: [{results_cs.overall_conf_int[0]:.4f}, {results_cs.overall_conf_int[1]:.4f}]\")\n", "\n", - "# By cohort (group_effects is populated by the fit-time aggregation - the\n", - "# documented route on this bootstrapped fit)\n", + "# By cohort - post-fit aggregation replays the fit-time bootstrap on this\n", + "# bootstrapped fit (percentile inference; no refit needed):\n", "print(\"\\nEffects by Adoption Cohort:\")\n", - "for cohort in sorted(results_cs.group_effects.keys()):\n", - " eff = results_cs.group_effects[cohort]\n", - " print(f\" Cohort {cohort}: {eff['effect']:>7.4f} (SE: {eff['se']:.4f})\")" + "by_cohort_cs = results_cs.aggregate('group').to_dataframe()\n", + "for _, row in by_cohort_cs.iterrows():\n", + " print(f\" Cohort {row['label']}: {row['att']:>7.4f} (SE: {row['se']:.4f})\")" ] }, { @@ -1418,10 +1371,10 @@ "id": "cell-24", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.957196Z", - "iopub.status.busy": "2026-08-10T22:40:33.957138Z", - "iopub.status.idle": "2026-08-10T22:40:33.959376Z", - "shell.execute_reply": "2026-08-10T22:40:33.959067Z" + "iopub.execute_input": "2026-08-18T20:30:55.854563Z", + "iopub.status.busy": "2026-08-18T20:30:55.854502Z", + "iopub.status.idle": "2026-08-18T20:30:55.859377Z", + "shell.execute_reply": "2026-08-18T20:30:55.859044Z" } }, "outputs": [ @@ -1433,36 +1386,38 @@ "============================================================\n", " Event Time ATT SE 95% CI\n", "------------------------------------------------------------\n", - " -8 1.6104 0.1450 [ 1.3413, 1.9084] *\n", - " -7 -0.7193 0.6174 [ -1.7550, 0.3490] \n", - " -6 0.6331 0.1778 [ 0.3244, 0.9672] *\n", - " -5 0.0078 0.2481 [ -0.4112, 0.5019] \n", - " -4 -0.3901 0.1963 [ -0.7535, -0.0320] *\n", - " -3 0.3038 0.1509 [ 0.0454, 0.6283] *\n", - " -2 -0.1471 0.1801 [ -0.5109, 0.2352] \n", - " -1 -0.3231 0.2424 [ -0.7721, 0.1501] \n", - " 0 0.4655 0.1637 [ 0.1527, 0.7322] *\n", - " 1 0.7781 0.2499 [ 0.2363, 1.2198] *\n", - " 2 0.4840 0.2533 [ 0.0231, 0.8852] *\n", - " 3 0.3673 0.2951 [ -0.1637, 0.8995] \n", - " 4 0.1541 0.3286 [ -0.4573, 0.7752] \n", - " 5 0.3210 0.1829 [ -0.0407, 0.6958] \n" + " -8 1.6104 0.1463 [ 1.3090, 1.9176] *\n", + " -7 -0.7193 0.6132 [ -1.7487, 0.2728] \n", + " -6 0.6331 0.1824 [ 0.3261, 0.9711] *\n", + " -5 0.0078 0.2376 [ -0.4458, 0.4203] \n", + " -4 -0.3901 0.1934 [ -0.7566, -0.0438] *\n", + " -3 0.3038 0.1842 [ -0.0423, 0.6573] \n", + " -2 -0.1471 0.1716 [ -0.4611, 0.1992] \n", + " -1 -0.3231 0.2249 [ -0.8173, 0.0790] \n", + " 0 0.4655 0.1667 [ 0.1794, 0.8112] *\n", + " 1 0.7781 0.2523 [ 0.2855, 1.2094] *\n", + " 2 0.4840 0.2361 [ 0.0154, 0.9215] \n", + " 3 0.3673 0.2930 [ -0.1773, 0.9063] \n", + " 4 0.1541 0.2809 [ -0.3371, 0.7341] \n", + " 5 0.3210 0.1902 [ -0.0138, 0.7406] \n" ] } ], "source": [ - "# Event study aggregation (event_study_effects is populated by the fit-time\n", - "# aggregation - the documented route on this bootstrapped fit)\n", + "# Event study aggregation - the post-fit replay works on bootstrapped fits:\n", + "es_cs = results_cs.aggregate('event_study')\n", + "\n", "print(\"Event Study Results (Effect by Years Since Adoption)\")\n", "print(\"=\" * 60)\n", "print(f\"{'Event Time':>12} {'ATT':>10} {'SE':>10} {'95% CI':>25}\")\n", "print(\"-\" * 60)\n", "\n", - "for e in sorted(results_cs.event_study_effects.keys()):\n", - " eff = results_cs.event_study_effects[e]\n", - " ci = eff['conf_int']\n", - " sig = '*' if eff['p_value'] < 0.05 else ''\n", - " print(f\"{e:>12} {eff['effect']:>10.4f} {eff['se']:>10.4f} [{ci[0]:>8.4f}, {ci[1]:>8.4f}] {sig}\")" + "for i, e in enumerate(es_cs.event_time):\n", + " if bool(es_cs.is_reference[i]):\n", + " continue\n", + " lo, hi = es_cs.conf_int_lower[i], es_cs.conf_int_upper[i]\n", + " sig = '*' if es_cs.p_value[i] < 0.05 else ''\n", + " print(f\"{e:>12} {es_cs.att[i]:>10.4f} {es_cs.se[i]:>10.4f} [{lo:>8.4f}, {hi:>8.4f}] {sig}\")" ] }, { @@ -1471,16 +1426,16 @@ "id": "cell-25", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:33.960241Z", - "iopub.status.busy": "2026-08-10T22:40:33.960184Z", - "iopub.status.idle": "2026-08-10T22:40:34.014322Z", - "shell.execute_reply": "2026-08-10T22:40:34.013779Z" + "iopub.execute_input": "2026-08-18T20:30:55.860205Z", + "iopub.status.busy": "2026-08-18T20:30:55.860145Z", + "iopub.status.idle": "2026-08-18T20:30:55.903579Z", + "shell.execute_reply": "2026-08-18T20:30:55.903184Z" } }, "outputs": [ { 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", 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", "text/plain": [ "
" ] @@ -1499,11 +1454,11 @@ } ], "source": [ - "# Event study visualization\n", + "# Event study visualization (plot_event_study accepts the post-fit container)\n", "if HAS_MATPLOTLIB:\n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " plot_event_study(\n", - " results=results_cs,\n", + " results=es_cs,\n", " ax=ax,\n", " title='Castle Doctrine Laws: Effect on Homicide Rates',\n", " xlabel='Years Since Law Adoption',\n", @@ -1529,10 +1484,10 @@ "id": "cell-27", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.015299Z", - "iopub.status.busy": "2026-08-10T22:40:34.015213Z", - "iopub.status.idle": "2026-08-10T22:40:34.033747Z", - "shell.execute_reply": "2026-08-10T22:40:34.033385Z" + "iopub.execute_input": "2026-08-18T20:30:55.904535Z", + "iopub.status.busy": "2026-08-18T20:30:55.904470Z", + "iopub.status.idle": "2026-08-18T20:30:55.923752Z", + "shell.execute_reply": "2026-08-18T20:30:55.923419Z" } }, "outputs": [ @@ -1609,10 +1564,10 @@ "id": "cell-28", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.034583Z", - "iopub.status.busy": "2026-08-10T22:40:34.034530Z", - "iopub.status.idle": "2026-08-10T22:40:34.036451Z", - "shell.execute_reply": "2026-08-10T22:40:34.036132Z" + "iopub.execute_input": "2026-08-18T20:30:55.924662Z", + "iopub.status.busy": "2026-08-18T20:30:55.924601Z", + "iopub.status.idle": "2026-08-18T20:30:55.926812Z", + "shell.execute_reply": "2026-08-18T20:30:55.926462Z" } }, "outputs": [ @@ -1624,7 +1579,7 @@ "============================================================\n", "Estimator Overall ATT SE\n", "------------------------------------------------------------\n", - "Callaway-Sant'Anna 0.4725 0.2071\n", + "Callaway-Sant'Anna 0.4725 0.1946\n", "Sun-Abraham 0.4725 0.2100\n", "TWFE (potentially biased) 0.0001 0.0001\n" ] @@ -1677,10 +1632,10 @@ "id": "cell-30", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.037197Z", - "iopub.status.busy": "2026-08-10T22:40:34.037145Z", - "iopub.status.idle": "2026-08-10T22:40:34.044453Z", - "shell.execute_reply": "2026-08-10T22:40:34.044136Z" + "iopub.execute_input": "2026-08-18T20:30:55.927757Z", + "iopub.status.busy": "2026-08-18T20:30:55.927702Z", + "iopub.status.idle": "2026-08-18T20:30:55.934988Z", + "shell.execute_reply": "2026-08-18T20:30:55.934632Z" } }, "outputs": [ @@ -1698,7 +1653,7 @@ "name": "stderr", "output_type": "stream", "text": [ - ".py:2: UserWarning: divorce_laws canonical data are unavailable (no verified canonical source is configured); returning a SYNTHETIC fallback. Check `df.attrs['source']` before treating the result as replication data.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51890/1542907809.py:2: UserWarning: divorce_laws canonical data are unavailable (no verified canonical source is configured); returning a SYNTHETIC fallback. Check `df.attrs['source']` before treating the result as replication data.\n", " divorce = load_divorce_laws()\n" ] }, @@ -1831,10 +1786,10 @@ "id": "cell-31", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.045311Z", - "iopub.status.busy": "2026-08-10T22:40:34.045258Z", - "iopub.status.idle": "2026-08-10T22:40:34.048000Z", - "shell.execute_reply": "2026-08-10T22:40:34.047686Z" + "iopub.execute_input": "2026-08-18T20:30:55.935931Z", + "iopub.status.busy": "2026-08-18T20:30:55.935876Z", + "iopub.status.idle": "2026-08-18T20:30:55.938671Z", + "shell.execute_reply": "2026-08-18T20:30:55.938329Z" } }, "outputs": [ @@ -1883,10 +1838,10 @@ "id": "cell-32", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.048763Z", - "iopub.status.busy": "2026-08-10T22:40:34.048711Z", - "iopub.status.idle": "2026-08-10T22:40:34.066530Z", - "shell.execute_reply": "2026-08-10T22:40:34.066275Z" + "iopub.execute_input": "2026-08-18T20:30:55.939552Z", + "iopub.status.busy": "2026-08-18T20:30:55.939494Z", + "iopub.status.idle": "2026-08-18T20:30:55.953017Z", + "shell.execute_reply": "2026-08-18T20:30:55.952732Z" } }, "outputs": [ @@ -1911,76 +1866,11 @@ "-------------------------------------------------------------------------------------\n", "Parameter Estimate Std. Err. t-stat P>|t| Sig.\n", "-------------------------------------------------------------------------------------\n", - "ATT 0.1917 0.0634 3.026 0.0050 **\n", - "-------------------------------------------------------------------------------------\n", - "\n", - "95% Confidence Interval: [0.0653, 0.2976]\n", - "CV (SE/abs(ATT)): 0.3305\n", - "\n", - "-------------------------------------------------------------------------------------\n", - " Event Study (Dynamic) Effects \n", - "-------------------------------------------------------------------------------------\n", - "Rel. Period Estimate Std. Err. t-stat P>|t| Sig.\n", - "-------------------------------------------------------------------------------------\n", - "-18 0.5375 0.1000 5.377 0.0050 **\n", - "-17 0.1462 0.1336 1.095 0.2814 \n", - "-16 -0.1481 0.1467 -1.010 0.3719 \n", - "-15 -0.0583 0.0904 -0.645 0.5025 \n", - "-14 -0.0813 0.0732 -1.110 0.2814 \n", - "-13 0.2450 0.1332 1.839 0.0402 *\n", - "-12 -0.1171 0.2741 -0.427 0.4422 \n", - "-11 -0.0200 0.2341 -0.085 0.8442 \n", - "-10 0.1929 0.0498 3.874 0.0050 **\n", - "-9 -0.4281 0.1837 -2.331 0.0050 **\n", - "-8 0.0960 0.2544 0.377 0.8543 \n", - "-7 0.0543 0.1041 0.521 0.5729 \n", - "-6 -0.0868 0.2327 -0.373 0.7136 \n", - "-5 -0.1825 0.1670 -1.093 0.3116 \n", - "-4 0.0112 0.1235 0.091 0.8241 \n", - "-3 -0.0630 0.0899 -0.700 0.4925 \n", - "-2 0.0836 0.0753 1.110 0.3116 \n", - "-1 -0.1068 0.0705 -1.516 0.0804 .\n", - "0 0.5596 0.0684 8.184 0.0050 **\n", - "1 0.5028 0.0713 7.053 0.0050 **\n", - "2 0.4596 0.0895 5.134 0.0050 **\n", - "3 0.2774 0.0900 3.082 0.0050 **\n", - "4 0.3598 0.0828 4.343 0.0050 **\n", - "5 0.3911 0.0999 3.915 0.0050 **\n", - "6 0.1070 0.0814 1.314 0.2613 \n", - "7 0.1279 0.0902 1.417 0.1508 \n", - "8 0.1368 0.0813 1.683 0.1005 \n", - "9 0.1452 0.0889 1.633 0.1307 \n", - "10 0.1646 0.0964 1.707 0.0804 .\n", - "11 0.0594 0.1103 0.539 0.5427 \n", - "12 -0.1192 0.1105 -1.078 0.2915 \n", - "13 -0.1261 0.0980 -1.287 0.2211 \n", - "14 0.0347 0.1076 0.323 0.7136 \n", - "15 -0.1038 0.0922 -1.126 0.2312 \n", - "16 -0.0682 0.1258 -0.542 0.5729 \n", - "17 0.1128 0.1500 0.752 0.4925 \n", - "18 0.2019 0.2676 0.754 0.5628 \n", - "19 -0.1137 0.1667 -0.682 0.4925 \n", + "ATT 0.1917 0.0614 3.121 0.0050 **\n", "-------------------------------------------------------------------------------------\n", - "Simult. CI: critical value = 2.8065 (sup-t bootstrap, 95% family-wise)\n", "\n", - "-------------------------------------------------------------------------------------\n", - " Effects by Treatment Cohort \n", - "-------------------------------------------------------------------------------------\n", - "Cohort Estimate Std. Err. t-stat P>|t| Sig.\n", - "-------------------------------------------------------------------------------------\n", - "1969 0.0254 0.1040 0.245 0.8744 \n", - "1970 0.5027 0.2189 2.297 0.0050 **\n", - "1971 0.1403 0.1228 1.142 0.2613 \n", - "1972 0.2876 0.1283 2.243 0.0050 **\n", - "1973 0.0888 0.0836 1.063 0.3920 \n", - "1974 0.3658 0.0680 5.377 0.0050 **\n", - "1975 0.0037 0.1374 0.027 0.9950 \n", - "1977 0.7881 0.1233 6.390 0.0050 **\n", - "1978 -0.1811 0.0586 -3.092 0.0050 **\n", - "1984 0.4695 0.1302 3.605 0.0050 **\n", - "1985 0.4806 0.0570 8.437 0.0050 **\n", - "1987 0.2475 0.1316 1.881 0.1005 \n", - "-------------------------------------------------------------------------------------\n", + "95% Confidence Interval: [0.0662, 0.2966]\n", + "CV (SE/abs(ATT)): 0.3204\n", "\n", "Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1\n", "=====================================================================================\n" @@ -1990,9 +1880,7 @@ "name": "stderr", "output_type": "stream", "text": [ - ".py:8: FutureWarning: CallawaySantAnna.fit(aggregate=) is deprecated and will be removed in 4.0. Fit once, then aggregate as a post-fit step: results = CallawaySantAnna().fit(...); results.aggregate('event_study') / .aggregate('group') / .aggregate('simple'). balance_e moves onto aggregate() alongside it: results.aggregate('event_study', balance_e=2).\n", - " results_divorce = cs_divorce.fit(\n", - ".py:8: UserWarning: 21 (group, time) cell(s) could not be estimated: 21 due to insufficient data or non-estimable cells.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_51890/2217541306.py:8: UserWarning: 21 (group, time) cell(s) could not be estimated: 21 due to insufficient data or non-estimable cells.\n", " results_divorce = cs_divorce.fit(\n" ] } @@ -2011,9 +1899,6 @@ " unit='state',\n", " time='year',\n", " first_treat='first_treat',\n", - " # Same bootstrapped-fit exception as the Castle Doctrine fit above:\n", - " # fit-time aggregate= is the documented route until 4.0.\n", - " aggregate='all'\n", ")\n", "\n", "print(results_divorce.summary())" @@ -2025,10 +1910,10 @@ "id": "cell-33", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.067430Z", - "iopub.status.busy": "2026-08-10T22:40:34.067378Z", - "iopub.status.idle": "2026-08-10T22:40:34.069683Z", - "shell.execute_reply": "2026-08-10T22:40:34.069399Z" + "iopub.execute_input": "2026-08-18T20:30:55.953915Z", + "iopub.status.busy": "2026-08-18T20:30:55.953863Z", + "iopub.status.idle": "2026-08-18T20:30:55.971805Z", + "shell.execute_reply": "2026-08-18T20:30:55.971478Z" } }, "outputs": [ @@ -2040,59 +1925,62 @@ "=================================================================\n", " Years Since Effect SE Significant\n", "-----------------------------------------------------------------\n", - " -18 0.5375 0.1000 Yes\n", - " -17 0.1462 0.1336 No\n", - " -16 -0.1481 0.1467 No\n", - " -15 -0.0583 0.0904 No\n", - " -14 -0.0813 0.0732 No\n", - " -13 0.2450 0.1332 Yes\n", - " -12 -0.1171 0.2741 No\n", - " -11 -0.0200 0.2341 No\n", - " -10 0.1929 0.0498 Yes\n", - " -9 -0.4281 0.1837 Yes\n", - " -8 0.0960 0.2544 No\n", - " -7 0.0543 0.1041 No\n", - " -6 -0.0868 0.2327 No\n", - " -5 -0.1825 0.1670 No\n", - " -4 0.0112 0.1235 No\n", - " -3 -0.0630 0.0899 No\n", - " -2 0.0836 0.0753 No\n", - " -1 -0.1068 0.0705 No\n", - " 0 0.5596 0.0684 Yes\n", - " 1 0.5028 0.0713 Yes\n", - " 2 0.4596 0.0895 Yes\n", - " 3 0.2774 0.0900 Yes\n", - " 4 0.3598 0.0828 Yes\n", - " 5 0.3911 0.0999 Yes\n", - " 6 0.1070 0.0814 No\n", - " 7 0.1279 0.0902 No\n", - " 8 0.1368 0.0813 No\n", - " 9 0.1452 0.0889 No\n", - " 10 0.1646 0.0964 No\n", - " 11 0.0594 0.1103 No\n", - " 12 -0.1192 0.1105 No\n", - " 13 -0.1261 0.0980 No\n", - " 14 0.0347 0.1076 No\n", - " 15 -0.1038 0.0922 No\n", - " 16 -0.0682 0.1258 No\n", - " 17 0.1128 0.1500 No\n", - " 18 0.2019 0.2676 No\n", - " 19 -0.1137 0.1667 No\n" + " -18 0.5375 0.0926 Yes\n", + " -17 0.1462 0.1367 No\n", + " -16 -0.1481 0.1482 No\n", + " -15 -0.0583 0.1043 No\n", + " -14 -0.0813 0.0810 No\n", + " -13 0.2450 0.1398 No\n", + " -12 -0.1171 0.3241 No\n", + " -11 -0.0200 0.2684 No\n", + " -10 0.1929 0.0528 Yes\n", + " -9 -0.4281 0.1862 Yes\n", + " -8 0.0960 0.2523 No\n", + " -7 0.0543 0.1146 No\n", + " -6 -0.0868 0.2335 No\n", + " -5 -0.1825 0.1792 No\n", + " -4 0.0112 0.1229 No\n", + " -3 -0.0630 0.0861 No\n", + " -2 0.0836 0.0782 No\n", + " -1 -0.1068 0.0674 No\n", + " 0 0.5596 0.0719 Yes\n", + " 1 0.5028 0.0774 Yes\n", + " 2 0.4596 0.0893 Yes\n", + " 3 0.2774 0.0826 Yes\n", + " 4 0.3598 0.0841 Yes\n", + " 5 0.3911 0.0926 Yes\n", + " 6 0.1070 0.0783 No\n", + " 7 0.1279 0.0811 No\n", + " 8 0.1368 0.0802 No\n", + " 9 0.1452 0.0895 No\n", + " 10 0.1646 0.0960 No\n", + " 11 0.0594 0.1033 No\n", + " 12 -0.1192 0.1003 No\n", + " 13 -0.1261 0.0997 No\n", + " 14 0.0347 0.1069 No\n", + " 15 -0.1038 0.0902 No\n", + " 16 -0.0682 0.1247 No\n", + " 17 0.1128 0.1472 No\n", + " 18 0.2019 0.2416 No\n", + " 19 -0.1137 0.1598 No\n" ] } ], "source": [ - "# Event study results (event_study_effects is populated by the fit-time\n", - "# aggregation - the documented route on this bootstrapped fit)\n", + "# Event study results - post-fit aggregation replays the fit-time\n", + "# bootstrap on this bootstrapped fit (percentile inference; no refit):\n", + "es_divorce = results_divorce.aggregate('event_study')\n", + "\n", "print(\"Event Study: Effect of Unilateral Divorce on Divorce Rates\")\n", "print(\"=\" * 65)\n", "print(f\"{'Years Since':>12} {'Effect':>10} {'SE':>10} {'Significant':>12}\")\n", "print(\"-\" * 65)\n", "\n", - "for e in sorted(results_divorce.event_study_effects.keys()):\n", - " eff = results_divorce.event_study_effects[e]\n", - " sig = 'Yes' if eff['p_value'] < 0.05 else 'No'\n", - " print(f\"{e:>12} {eff['effect']:>10.4f} {eff['se']:>10.4f} {sig:>12}\")" + "for i, e in enumerate(es_divorce.event_time):\n", + " if bool(es_divorce.is_reference[i]):\n", + " continue\n", + " sig = 'Yes' if es_divorce.p_value[i] < 0.05 else 'No'\n", + " print(f\"{e:>12} {es_divorce.att[i]:>10.4f} {es_divorce.se[i]:>10.4f} {sig:>12}\")" ] }, { @@ -2101,16 +1989,16 @@ "id": "cell-34", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.070490Z", - "iopub.status.busy": "2026-08-10T22:40:34.070431Z", - "iopub.status.idle": "2026-08-10T22:40:34.158849Z", - "shell.execute_reply": "2026-08-10T22:40:34.158462Z" + "iopub.execute_input": "2026-08-18T20:30:55.972747Z", + "iopub.status.busy": "2026-08-18T20:30:55.972686Z", + "iopub.status.idle": "2026-08-18T20:30:56.038730Z", + "shell.execute_reply": "2026-08-18T20:30:56.038313Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2129,11 +2017,11 @@ } ], "source": [ - "# Event study visualization\n", + "# Event study visualization (plot_event_study accepts the post-fit container)\n", "if HAS_MATPLOTLIB:\n", " fig, ax = plt.subplots(figsize=(12, 6))\n", " plot_event_study(\n", - " results=results_divorce,\n", + " results=es_divorce,\n", " ax=ax,\n", " title='Unilateral Divorce Laws: Effect on Divorce Rates',\n", " xlabel='Years Since Law Adoption',\n", @@ -2163,10 +2051,10 @@ "id": "cell-36", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:34.159893Z", - "iopub.status.busy": "2026-08-10T22:40:34.159815Z", - "iopub.status.idle": "2026-08-10T22:40:34.161708Z", - "shell.execute_reply": "2026-08-10T22:40:34.161370Z" + "iopub.execute_input": "2026-08-18T20:30:56.039675Z", + "iopub.status.busy": "2026-08-18T20:30:56.039603Z", + "iopub.status.idle": "2026-08-18T20:30:56.045337Z", + "shell.execute_reply": "2026-08-18T20:30:56.045025Z" } }, "outputs": [ @@ -2176,31 +2064,30 @@ "text": [ "Effects by Adoption Cohort\n", "==================================================\n", - "Cohort 1969: 0.0254 (SE: 0.1040) \n", - "Cohort 1970: 0.5027 (SE: 0.2189) *\n", - "Cohort 1971: 0.1403 (SE: 0.1228) \n", - "Cohort 1972: 0.2876 (SE: 0.1283) *\n", - "Cohort 1973: 0.0888 (SE: 0.0836) \n", - "Cohort 1974: 0.3658 (SE: 0.0680) *\n", - "Cohort 1975: 0.0037 (SE: 0.1374) \n", - "Cohort 1977: 0.7881 (SE: 0.1233) *\n", - "Cohort 1978: -0.1811 (SE: 0.0586) *\n", - "Cohort 1984: 0.4695 (SE: 0.1302) *\n", - "Cohort 1985: 0.4806 (SE: 0.0570) *\n", - "Cohort 1987: 0.2475 (SE: 0.1316) \n" + "Cohort 1969: 0.0254 (SE: 0.1025) \n", + "Cohort 1970: 0.5027 (SE: 0.2376) *\n", + "Cohort 1971: 0.1403 (SE: 0.1353) \n", + "Cohort 1972: 0.2876 (SE: 0.1286) *\n", + "Cohort 1973: 0.0888 (SE: 0.0796) \n", + "Cohort 1974: 0.3658 (SE: 0.0690) *\n", + "Cohort 1975: 0.0037 (SE: 0.1268) \n", + "Cohort 1977: 0.7881 (SE: 0.1255) *\n", + "Cohort 1978: -0.1811 (SE: 0.0582) *\n", + "Cohort 1984: 0.4695 (SE: 0.1227) *\n", + "Cohort 1985: 0.4806 (SE: 0.0611) *\n", + "Cohort 1987: 0.2475 (SE: 0.1228) \n" ] } ], "source": [ - "# Effects by cohort (group_effects is populated by the fit-time aggregation -\n", - "# the documented route on this bootstrapped fit)\n", + "# Effects by cohort - post-fit aggregation (bootstrap replay):\n", "print(\"Effects by Adoption Cohort\")\n", "print(\"=\" * 50)\n", "\n", - "for cohort in sorted(results_divorce.group_effects.keys()):\n", - " eff = results_divorce.group_effects[cohort]\n", - " sig = '*' if eff['p_value'] < 0.05 else ''\n", - " print(f\"Cohort {cohort}: {eff['effect']:>7.4f} (SE: {eff['se']:.4f}) {sig}\")" + "by_cohort_divorce = results_divorce.aggregate('group').to_dataframe()\n", + "for _, row in by_cohort_divorce.iterrows():\n", + " sig = '*' if row['p_value'] < 0.05 else ''\n", + " print(f\"Cohort {row['label']}: {row['att']:>7.4f} (SE: {row['se']:.4f}) {sig}\")" ] }, { diff --git a/docs/v4-deprecations.yaml b/docs/v4-deprecations.yaml index bc4b0d196..79c046c7b 100644 --- a/docs/v4-deprecations.yaml +++ b/docs/v4-deprecations.yaml @@ -228,8 +228,8 @@ rows: phase: 5 warning: FutureWarning test_ref: tests/test_aggregate_contract.py - code_refs: [diff_diff/staggered.py, diff_diff/staggered_results.py, diff_diff/aggregation.py, diff_diff/practitioner.py, diff_diff/guides/llms-practitioner.txt, diff_diff/diagnostic_report.py] - notes: "Shimmed in 3.9: fit(aggregate=) warns via a sentinel default (so a plain fit() never warns) and still returns the fully populated legacy surface; results.aggregate(type=) is the successor. balance_e moves alongside it as its own row [M-117] - it was previously tracked only as prose here, which nothing asserted. VOCABULARY: the closed set is library-wide (simple|event_study|group|calendar|total - 'total' promoted 2026-08-16 as the estimator-owned total incremental outcome, an exact relay C x overall over the finite-masked complete-case support); CallawaySantAnna's SUPPORTED SUBSET is simple|event_study|group|total - it has no calendar aggregator (the DEFERRED 'Calendar-time aggregation' row), and aggregate('calendar') raises naming what is supported; 'total' is panel/non-survey only (RC-routed, declared-survey_design, divergent bare-cluster, and pre-upgrade-kit fits raise NotImplementedError naming the reason; the mass replays from the fit-time agg_gt_cells/is_survey_fit kit snapshots). BOOTSTRAP fits: 'simple' and, where supported, 'total' RELAY the stored overall quintet verbatim (percentile se/p/CI beside the finite safe_inference t; 'total' scales att/se/CI by C) with a NaN df column, while the recompute levels (event_study/group) fail closed pending draw retention (BootstrapReplaySpec is the TODO row) - the per-level policy converged with [M-027] (previously ALL levels failed closed; the relay never publishes an analytical df beside percentile inference). DiagnosticReport now derives the event-study surface via post-fit aggregate('event_study') when the raw field is absent, so its ES-gated checks run on plain fits (derivation failures surface as explicit skip reasons)." + code_refs: [diff_diff/staggered.py, diff_diff/staggered_results.py, diff_diff/staggered_bootstrap.py, diff_diff/bootstrap_chunking.py, diff_diff/aggregation.py, diff_diff/practitioner.py, diff_diff/guides/llms-practitioner.txt, diff_diff/diagnostic_report.py] + notes: "Shimmed in 3.9: fit(aggregate=) warns via a sentinel default (so a plain fit() never warns) and still returns the fully populated legacy surface; results.aggregate(type=) is the successor. balance_e moves alongside it as its own row [M-117] - it was previously tracked only as prose here, which nothing asserted. VOCABULARY: the closed set is library-wide (simple|event_study|group|calendar|total - 'total' promoted 2026-08-16 as the estimator-owned total incremental outcome, an exact relay C x overall over the finite-masked complete-case support); CallawaySantAnna's SUPPORTED SUBSET is simple|event_study|group|total - it has no calendar aggregator (the DEFERRED 'Calendar-time aggregation' row), and aggregate('calendar') raises naming what is supported; 'total' is panel/non-survey only (RC-routed, declared-survey_design, divergent bare-cluster, and pre-upgrade-kit fits raise NotImplementedError naming the reason; the mass replays from the fit-time agg_gt_cells/is_survey_fit kit snapshots). BOOTSTRAP fits: 'simple' and, where supported, 'total' RELAY the stored overall quintet verbatim (percentile se/p/CI beside the finite safe_inference t; 'total' scales att/se/CI by C) with a NaN df column - the relay levels never re-warn; the recompute levels (event_study/group) REPLAY the fit-time multiplier bootstrap from the kit's BootstrapReplaySpec (the fit-captured RNG state + run params BY VALUE, so seed=None fits replay and post-fit set_params/attribute mutation cannot alter it; pickles carry it): se/CI/cband match a fit-time aggregation to BLAS reassociation (~1 ULP, assert_allclose - never bit-identity), the discrete percentile P-VALUE is a count statistic carved out of that claim (compared at 2/n_bootstrap), and the container publishes NO analytical provenance (vcov/vcov_index/df cleared, sup-t cband recomputed from the replayed draws). Each replaying call regenerates the full weight stream and re-runs the fused perturbation GEMM over the per-cell + per-event-time influence columns - O(n_bootstrap x n_units x (n_gt + n_event_times)) FLOPs per call, no memoization (immutability discipline; DR caches its derived surface once per report). The replay re-runs the same warning sites (never suppresses; per-site Python warning-registry semantics govern re-display). BACKEND GUARD: the spec stamps the weight-generation backend at capture ('rust'/'numpy' per bootstrap_chunking.effective_weight_backend, or 'portable' for provably backend-independent branches - stratified/single-PSU survey generation and census-FPC zero weights); Rust and NumPy produce DIFFERENT draws from the same bit-generator state, so a replay under a different backend (DIFF_DIFF_BACKEND flip, missing extension, another machine) fails closed naming both backends rather than silently desynchronizing from the artifact's stored relay quintet. Legacy pickles without the spec fail closed with a refit message. The per-level policy converged with [M-027]; the SDDD engine keeps its own fit-time override-loop copy (unification sequenced with the M-014 container port - twin-drift risk on record). DiagnosticReport now derives the event-study surface via post-fit aggregate('event_study') when the raw field is absent, so its ES-gated checks run on plain fits BOOTSTRAPPED INCLUDED (percentile replay; parallel_trends rides the Bonferroni fallback, pretrends_power/sensitivity the diagonal-covariance fallback - the derived container has vcov=None); derivation failures (kit-less/legacy pickles, backend mismatch, sibling estimators' gates) still surface as explicit skip reasons." - id: M-021 kind: param group: aggregate-postfit diff --git a/docs/v4-design.md b/docs/v4-design.md index 433144291..eb61a87c5 100644 --- a/docs/v4-design.md +++ b/docs/v4-design.md @@ -987,7 +987,8 @@ five recorded deviations: - **No `Available since` column.** Considered and rejected: it cannot be derived. `introduced_in` tracks the dataclass storage flip, so the nine `field-flip` rows say `4.0` while `.att` already resolves today; and a successor that exists can - still raise (the five bootstrapped-fit `aggregate()` families). Availability is + still raise (the bootstrapped-fit `aggregate()` recompute gates — four families + since CS's percentile-bootstrap replay landed). Availability is stated in prose where it is verifiable instead. - **§7b "Remaining 4.0 changes"** was added: §§2-8 as skeletoned cover only 102 of the 108 qualifying rows, leaving `obligation-sdid-params`, `constructor-hygiene`, diff --git a/tests/test_aggregate_contract.py b/tests/test_aggregate_contract.py index 5ecf978d2..95cfca263 100644 --- a/tests/test_aggregate_contract.py +++ b/tests/test_aggregate_contract.py @@ -301,20 +301,6 @@ def test_balance_e_rejected_where_inert(self, fitted, level): with pytest.raises(ValueError, match="balance_e"): fitted.aggregate(level, balance_e=2) - def test_bootstrap_recompute_levels_fail_closed(self, panel): - """Percentile-bootstrap inference cannot be reproduced from the - analytical state retained here, so the RECOMPUTE levels must not - substitute analytical numbers silently.""" - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - boot = CallawaySantAnna(n_bootstrap=49, seed=42).fit(panel, **FIT_KW) - for level in ("event_study", "group"): - with pytest.raises(NotImplementedError, match="bootstrap") as exc: - boot.aggregate(level) - assert "aggregate('simple') and, where supported, aggregate('total') relay" in str( - exc.value - ) - def test_bootstrap_simple_relays_stored_quintet(self, panel): """Per-level policy (converged with M-027): 'simple' is a bit-exact relay of the stored overall row, faithful under the bootstrap regime, @@ -348,6 +334,359 @@ def test_bootstrap_survey_simple_relay_df_nan(self, panel): _assert_bootstrap_simple_relay(boot) +# --------------------------------------------------------------------------- # +# Bootstrap replay: recompute levels on bootstrapped fits +# --------------------------------------------------------------------------- # + + +def _boot_fit(data, *, seed=42, n_bootstrap=50, fit_kwargs=None, **ctor): + """A plain bootstrapped fit (modern route: no fit-time aggregate=).""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return CallawaySantAnna(n_bootstrap=n_bootstrap, seed=seed, **ctor).fit( + data, **FIT_KW, **(fit_kwargs or {}) + ) + + +def _boot_fit_time(data, *, seed=42, n_bootstrap=50, aggregate="all", fit_kwargs=None, **ctor): + """The parity REFERENCE: the deprecated fit-time aggregation route. + + The reference is the NATIVE fit-time surface (`event_study_effects` / + `group_effects` dict entries + `cband_crit_value`), never a second + aggregate() call — the kit is attached unconditionally at the end of + fit(), so a fit-time-aggregated result's own aggregate() would ALSO + replay, and replay-vs-replay proves nothing. + """ + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return CallawaySantAnna(n_bootstrap=n_bootstrap, seed=seed, **ctor).fit( + data, aggregate=aggregate, **FIT_KW, **(fit_kwargs or {}) + ) + + +def _assert_es_replay_parity(es, ref, n_bootstrap=50): + """Replayed EventStudyResults vs the native fit-time bootstrap surface. + + Tolerance note (reconciling with this file's 1e-14 analytical-parity + convention): the replayed weight stream is bit-identical, and only the + fused GEMM's tile-boundary/column-count reassociation differs between + the fit-time and replay passes — ~1 ULP relative — so 1e-13 is the same + contract with one order of headroom for quantile-interpolation + arithmetic; any real desynchronization is O(1), not O(1e-13). p-values + are COUNT statistics (a draw within a ULP of the point estimate can + flip one count), hence the additional 2/n_bootstrap atol. + """ + ref_es = ref.event_study_effects + p_atol = 2.0 / n_bootstrap + for i, e in enumerate(es.event_time): + if bool(es.is_reference[i]): + continue + r = ref_es[int(e)] + assert float(es.att[i]) == float(r["effect"]) # same analytical point path + np.testing.assert_allclose(es.se[i], r["se"], rtol=1e-13, atol=1e-13) + np.testing.assert_allclose(es.t_stat[i], r["t_stat"], rtol=1e-13, atol=1e-13) + np.testing.assert_allclose(es.p_value[i], r["p_value"], rtol=1e-13, atol=p_atol) + np.testing.assert_allclose( + [es.conf_int_lower[i], es.conf_int_upper[i]], + list(r["conf_int"]), + rtol=1e-13, + atol=1e-13, + ) + cb = r.get("cband_conf_int") + if cb is not None: + np.testing.assert_allclose( + [es.cband_lower[i], es.cband_upper[i]], list(cb), rtol=1e-13, atol=1e-13 + ) + # Percentile provenance: no joint covariance, no analytical df. + assert es.vcov is None + assert np.all(np.isnan(np.asarray(es.df, dtype=float))) + if ref.cband_crit_value is not None: + np.testing.assert_allclose(es.cband_crit_value, ref.cband_crit_value, rtol=1e-13) + + +def _assert_group_replay_parity(agg, ref, n_bootstrap=50): + gdf = agg.to_dataframe() + p_atol = 2.0 / n_bootstrap + for _, row in gdf.iterrows(): + r = ref.group_effects[row["label"]] + assert float(row["att"]) == float(r["effect"]) + if np.isnan(r["se"]): + assert np.isnan(row["se"]) + continue + np.testing.assert_allclose(row["se"], r["se"], rtol=1e-13, atol=1e-13) + np.testing.assert_allclose(row["t_stat"], r["t_stat"], rtol=1e-13, atol=1e-13) + np.testing.assert_allclose(row["p_value"], r["p_value"], rtol=1e-13, atol=p_atol) + # df cleared: percentile inference never used the analytical df. + assert np.isnan(row["df"]) + + +class TestBootstrapReplay: + """Recompute levels replay the fit-time multiplier bootstrap. + + Warning-coverage note (recorded decision, not a deferral): of the + warnings the wholesale re-run can re-emit, the two cheaply reachable + representatives — n_bootstrap<50 and G<2-PSU — are pinned below; the + degenerate-path warnings ("No post-treatment effects for bootstrap + aggregation", "Too few valid sup-t bootstrap samples") have no cheap + fixture (the first needs a fit whose every surviving cell is + pre-treatment, the second >50% non-finite sup-t draws) and are covered + by the convention, not pinned individually. + """ + + def test_event_study_parity_with_fit_time(self, panel): + ref = _boot_fit_time(panel, aggregate="event_study") + plain = _boot_fit(panel) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + es = plain.aggregate("event_study") + assert isinstance(es, EventStudyResults) + _assert_es_replay_parity(es, ref) + + def test_group_parity_with_fit_time(self, panel): + ref = _boot_fit_time(panel, aggregate="group") + plain = _boot_fit(panel) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + agg = plain.aggregate("group") + _assert_group_replay_parity(agg, ref) + + def test_balance_e_parity_with_fit_time(self, panel): + ref = _boot_fit_time(panel, aggregate="event_study", fit_kwargs={"balance_e": 1}) + plain = _boot_fit(panel) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + es = plain.aggregate("event_study", balance_e=1) + _assert_es_replay_parity(es, ref) + + def test_seedless_fit_replays_and_is_idempotent(self, panel): + """seed=None fits replay too — the spec captures the actual RNG + state by value — and repeated calls restore the same stream.""" + plain = _boot_fit(panel, seed=None) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + a = plain.aggregate("event_study") + b = plain.aggregate("event_study") + np.testing.assert_array_equal(a.se, b.se) + np.testing.assert_array_equal(a.p_value, b.p_value) + + def test_set_params_and_mutation_immunity(self, panel): + """The spec is by-value: post-fit set_params / direct attribute + mutation of the estimator cannot change the replay.""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + est = CallawaySantAnna(n_bootstrap=50, seed=42) + res = est.fit(panel, **FIT_KW) + before = res.aggregate("event_study") + est.set_params(n_bootstrap=5, bootstrap_weights="webb") + est.seed = 7 + after = res.aggregate("event_study") + np.testing.assert_array_equal(before.se, after.se) + + def test_pickle_round_trip_replays(self, panel): + """The PCG64 state dict pickles; an unpickled result replays + identically in the same environment (same weight backend).""" + plain = _boot_fit(panel) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + es = plain.aggregate("event_study") + rt = pickle.loads(pickle.dumps(plain)) + es_rt = rt.aggregate("event_study") + np.testing.assert_array_equal(es.se, es_rt.se) + + def test_relays_unchanged_and_order_independent(self, panel): + """'simple' still relays the stored quintet bit-exactly AFTER an ES + replay call (the replay writes nothing back to the results).""" + plain = _boot_fit(panel, n_bootstrap=49) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + plain.aggregate("event_study") + _assert_bootstrap_simple_relay(plain) + + def test_legacy_kit_without_spec_fails_closed(self, panel): + plain = _boot_fit(panel) + plain._aggregation_kit.bootstrap = None # simulate a pre-replay pickle + for level in ("event_study", "group"): + with pytest.raises(NotImplementedError, match="predates") as exc: + plain.aggregate(level) + assert "refit" in str(exc.value) + + def test_backend_mismatch_fails_closed(self, panel): + """A different weight backend regenerates different draws from the + same RNG state — the replay must refuse, deterministically under + either installed backend.""" + import dataclasses + + from diff_diff.bootstrap_chunking import effective_weight_backend + + plain = _boot_fit(panel) + spec = plain._aggregation_kit.bootstrap + assert spec.backend == effective_weight_backend() + other = "numpy" if spec.backend == "rust" else "rust" + plain._aggregation_kit.bootstrap = dataclasses.replace(spec, backend=other) + with pytest.raises(NotImplementedError, match="weight backend"): + plain.aggregate("event_study") + # None means UNKNOWN and also fails closed (fail-open default on a + # safety discriminator would disarm the guard for future callers). + plain._aggregation_kit.bootstrap = dataclasses.replace(spec, backend=None) + with pytest.raises(NotImplementedError, match="weight backend"): + plain.aggregate("group") + + def test_low_bootstrap_warning_re_emitted_on_replay(self, panel): + """The replay re-runs the fit-time engine, so its warnings re-fire + (the recompute-level re-warn convention; relays stay silent).""" + plain = _boot_fit(panel, n_bootstrap=49) + with pytest.warns(UserWarning, match="n_bootstrap=49 is low"): + plain.aggregate("event_study") + + def test_no_post_treatment_cells_group_returns_zero_rows(self): + """A fit whose every treated cohort's onset lies beyond the observed + panel has NO post-treatment cells: overall inference is NaN, only + pre-treatment cells exist, and the group aggregation is empty. The + bootstrapped group path must return the supported zero-row result on + BOTH routes (they share `_run_multiplier_bootstrap`, whose group + stats block would otherwise np.column_stack an empty list).""" + rng = np.random.default_rng(0) + rows = [] + for u in range(30): + g = 8 if u % 3 else 0 # onset beyond the 5-period panel + for t in range(1, 6): + rows.append( + { + "unit": u, + "time": t, + "first_treat": g, + "y": 0.3 * t + rng.normal(0, 0.3), + } + ) + data = pd.DataFrame(rows) + plain = _boot_fit(data, n_bootstrap=25, seed=1) + assert np.isnan(plain.overall_att) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + agg = plain.aggregate("group") + assert len(agg.to_dataframe()) == 0 + assert np.isnan(plain.overall_att) # unchanged by the replay + # The deprecated fit-time route rides the same helper — it must + # survive too (the crash was reachable there pre-replay). + ref = _boot_fit_time(data, n_bootstrap=25, seed=1, aggregate="group") + assert ref.group_effects in (None, {}) + + +class TestBootstrapReplayDesigns: + """Replay parity on the engine branches the plain panel never reaches.""" + + def test_bare_cluster_psu_expansion(self, panel): + d = panel.copy() + d["psu"] = d["unit"] % 7 + ref = _boot_fit_time(d, cluster="psu") + plain = _boot_fit(d, cluster="psu") + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + _assert_es_replay_parity(plain.aggregate("event_study"), ref) + _assert_group_replay_parity(plain.aggregate("group"), ref) + + def test_stratified_survey_is_portable_and_matches(self, panel): + from diff_diff import SurveyDesign + + d = panel.copy() + rng = np.random.default_rng(5) + wmap = {u: rng.uniform(0.5, 2.0) for u in d["unit"].unique()} + d["w"] = d["unit"].map(wmap) + d["psu"] = d["unit"] % 7 + d["stratum"] = d["unit"] % 2 + sd = SurveyDesign(weights="w", strata="stratum", psu="psu", nest=True) + ref = _boot_fit_time(d, fit_kwargs={"survey_design": sd}) + plain = _boot_fit(d, fit_kwargs={"survey_design": sd}) + # The stratified survey generator draws through NumPy regardless of + # the installed backend — provably backend-independent, stamped + # "portable" so the artifact replays anywhere. + assert plain._aggregation_kit.bootstrap.backend == "portable" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + _assert_es_replay_parity(plain.aggregate("event_study"), ref) + _assert_group_replay_parity(plain.aggregate("group"), ref) + + def test_fpc_survey_matches(self, panel): + from diff_diff import SurveyDesign + + d = panel.copy() + rng = np.random.default_rng(6) + wmap = {u: rng.uniform(0.5, 2.0) for u in d["unit"].unique()} + d["w"] = d["unit"].map(wmap) + d["psu"] = d["unit"] % 7 + d["fpc"] = 100 # non-census: the fpc_scale branch + sd = SurveyDesign(weights="w", psu="psu", fpc="fpc") + ref = _boot_fit_time(d, fit_kwargs={"survey_design": sd}) + plain = _boot_fit(d, fit_kwargs={"survey_design": sd}) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + _assert_es_replay_parity(plain.aggregate("event_study"), ref) + + def test_repeated_cross_sections_match(self): + data = _rcs() + ref = _boot_fit_time(data, panel=False) + plain = _boot_fit(data, panel=False) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + _assert_es_replay_parity(plain.aggregate("event_study"), ref) + _assert_group_replay_parity(plain.aggregate("group"), ref) + + def test_unbalanced_panel_matches(self, panel): + thinned = panel.drop(panel.index[::13]).reset_index(drop=True) + ref = _boot_fit_time(thinned, allow_unbalanced_panel=True) + plain = _boot_fit(thinned, allow_unbalanced_panel=True) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + _assert_es_replay_parity(plain.aggregate("event_study"), ref) + + def test_single_psu_nan_surfaces_and_warning(self, panel): + from diff_diff import SurveyDesign + + d = panel.copy() + rng = np.random.default_rng(8) + wmap = {u: rng.uniform(0.5, 2.0) for u in d["unit"].unique()} + d["w"] = d["unit"].map(wmap) + d["one_psu"] = 1 + single = _boot_fit( + d, fit_kwargs={"survey_design": SurveyDesign(weights="w", psu="one_psu")} + ) + # Single-PSU generation is backend-independent (degenerate branch). + assert single._aggregation_kit.bootstrap.backend == "portable" + with pytest.warns(UserWarning, match="PSU"): + es = single.aggregate("event_study") + nonref = ~np.asarray(es.is_reference, dtype=bool) + assert np.all(~np.isfinite(np.asarray(es.se, dtype=float)[nonref])) + assert es.cband_crit_value is None + + +class TestBootstrapReplayConsumers: + """The newly-reachable public path: a bootstrapped-CS derived container + (vcov=None) flowing into the event-study consumers.""" + + def test_pretrends_power_rides_diag_fallback(self, panel): + from diff_diff import compute_pretrends_power + + plain = _boot_fit(panel) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + es = plain.aggregate("event_study") + power = compute_pretrends_power(es) + # Silent by design on the pretrends side: the diagonal fallback is + # the container contract for vcov=None surfaces. + assert power is not None + + def test_honest_did_warns_then_uses_diagonal(self, panel): + from diff_diff import compute_honest_did + + plain = _boot_fit(panel) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + es = plain.aggregate("event_study") + with pytest.warns(UserWarning, match="no full covariance"): + honest = compute_honest_did(es, method="relative_magnitude", M=1.0) + assert honest is not None + + # --------------------------------------------------------------------------- # # Container schema (M-122) # --------------------------------------------------------------------------- # @@ -4205,7 +4544,8 @@ def test_universal_base_reference_cells_excluded(self, panel): def test_zero_c_arms_emit_all_nan_row_without_warning(self, panel): """Empty post set / all-NaN effects -> all-NaN row, NO UserWarning - (the no-post-fit-re-warn convention), on the plain-panel branch AND + (the RELAY-level no-re-warn convention; the recompute levels' + bootstrap replay re-emits by design), on the plain-panel branch AND the cohort-mass (bare-cluster) branch - the isfinite(C) guard must run BEFORE the coincidence comparison (NaN != 0.0 is True).""" for ctor in ({}, {"cluster": "unit"}): diff --git a/tests/test_bootstrap_chunking.py b/tests/test_bootstrap_chunking.py index db5316fde..634ec0f26 100644 --- a/tests/test_bootstrap_chunking.py +++ b/tests/test_bootstrap_chunking.py @@ -882,3 +882,39 @@ def _crit(): monkeypatch.setattr("diff_diff.bootstrap_chunking.compute_block_size", lambda *a, **k: 9) tiny = _crit() assert tiny == pytest.approx(base, rel=1e-8, abs=1e-10) + + +class TestEffectiveWeightBackend: + """The backend discriminator behind the post-fit bootstrap replay guard. + + Its predicate must stay IDENTICAL to iter_weight_blocks' own rust_gen + resolution — a degenerate helper (e.g. always "numpy") would silently + disarm the fail-closed backend-mismatch guard in + CallawaySantAnnaResults.aggregate(), so the return value is pinned + directly here rather than only through the guard. + """ + + def test_matches_the_iter_weight_blocks_predicate(self): + import diff_diff.bootstrap_chunking as bc + + expected = ( + "rust" if (bc.HAS_RUST_BACKEND and bc._rust_bootstrap_weights is not None) else "numpy" + ) + assert bc.effective_weight_backend() == expected + + def test_numpy_when_rust_unavailable(self, monkeypatch): + import diff_diff.bootstrap_chunking as bc + + monkeypatch.setattr(bc, "HAS_RUST_BACKEND", False) + monkeypatch.setattr(bc, "_rust_bootstrap_weights", None) + assert bc.effective_weight_backend() == "numpy" + + def test_rust_requires_both_flag_and_symbol(self, monkeypatch): + import diff_diff.bootstrap_chunking as bc + + # A stale-extension environment can have the flag without the + # symbol; the generator falls back to NumPy there, so the + # discriminator must too. + monkeypatch.setattr(bc, "HAS_RUST_BACKEND", True) + monkeypatch.setattr(bc, "_rust_bootstrap_weights", None) + assert bc.effective_weight_backend() == "numpy" diff --git a/tests/test_diagnostic_report.py b/tests/test_diagnostic_report.py index 53be69b68..7a4c386b2 100644 --- a/tests/test_diagnostic_report.py +++ b/tests/test_diagnostic_report.py @@ -3562,14 +3562,42 @@ def test_derived_route_emits_pre_period_source(self, cs_plain_fit, cs_fit): assert "pre_period_source" not in d_raw["pretrends_power"] assert "pre_period_source" not in d_raw["sensitivity"] - def test_bootstrap_plain_cs_skips_with_honest_reason(self, cs_bootstrap_plain_fit): + def test_bootstrap_plain_cs_derives_and_runs(self, cs_bootstrap_plain_fit): + """Bootstrapped plain CS fits now DERIVE the event-study surface via + the post-fit percentile-bootstrap replay (previously an explicit + skip). Expected oracle, confirmed empirically: the derived container + carries vcov=None (fit's bootstrap clearing rule), so + parallel_trends runs via the Bonferroni per-period fallback and + pretrends_power/sensitivity ride the diagonal-covariance fallback + (`_extract_container_vcov_subblock` returns + (np.diag(ses**2), "diag_fallback") when surface.vcov is None).""" cs, _ = cs_bootstrap_plain_fit - skipped = DiagnosticReport(cs).run_all().skipped_checks + rep = DiagnosticReport(cs).run_all() + schema = rep.schema for check in ("parallel_trends", "pretrends_power", "sensitivity"): - assert check in skipped, skipped - assert "aggregate('event_study')" in skipped[check] - assert "bootstrap" in skipped[check] - assert "deprecated" not in skipped[check] + assert check not in rep.skipped_checks, rep.skipped_checks + sec = schema[check] + assert sec.get("status") == "ran" + assert sec.get("pre_period_source") == "aggregate_event_study" + assert "deprecated" not in str(sec) + assert schema["parallel_trends"]["method"] == "bonferroni" + + def test_bootstrap_plain_cs_republishes_replay_warnings(self): + """The replay re-emits the fit-time bootstrap warnings; the derived + route records and republishes them per consuming section. Dedicated + 49-draw fixture: the low-draws warning gate is strictly < 50, so the + shared 50-draw module fixture cannot exercise republication.""" + sdf = generate_staggered_data(n_units=100, n_periods=6, treatment_effect=1.5, seed=7) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + cs = CallawaySantAnna(base_period="universal", n_bootstrap=49, seed=1).fit( + sdf, outcome="outcome", unit="unit", time="period", first_treat="first_treat" + ) + rep = DiagnosticReport(cs).run_all() + sec = rep.schema["parallel_trends"] + assert sec.get("status") == "ran" + republished = " ".join(str(w) for w in (sec.get("warnings") or [])) + assert "n_bootstrap=49 is low" in republished def test_missing_kit_value_error_leg(self, cs_plain_fit): import copy diff --git a/tests/test_practitioner.py b/tests/test_practitioner.py index ae604171c..8b911ce2f 100644 --- a/tests/test_practitioner.py +++ b/tests/test_practitioner.py @@ -293,6 +293,36 @@ def test_cs_results(self, cs_results): assert len(output["next_steps"]) > 0 assert output["estimator"] == "CallawaySantAnna" + def test_cs_bootstrapped_results_advise_post_fit_aggregate(self, staggered_data): + """Bootstrapped CS fits are advised through the SAME post-fit + aggregate() route (the percentile-bootstrap replay) — never the + deprecated fit-time aggregate= kwarg the pre-replay guidance + steered them to.""" + import warnings as _warnings + + with _warnings.catch_warnings(): + _warnings.simplefilter("ignore") + boot = CallawaySantAnna(n_bootstrap=25, seed=3).fit( + staggered_data, + outcome="outcome", + unit="unit", + time="period", + first_treat="first_treat", + ) + output = practitioner_next_steps(boot, verbose=False) + by_label = {s["label"]: s for s in output["next_steps"]} + sens = by_label["Run HonestDiD sensitivity analysis"] + het = by_label["Examine group and event study effects"] + assert "replays the fit-time multiplier bootstrap" in sens["why"] + assert "results.aggregate('event_study')" in sens["code"] + # Positive assertion on the heterogeneity code: BOTH post-fit levels. + assert "aggregate('group')" in het["code"] + assert "aggregate('event_study')" in het["code"] + # The fit-time kwarg form must be gone from the whole advice bundle. + for step in output["next_steps"]: + assert "aggregate='" not in step["code"], step["label"] + assert 'aggregate="' not in step["code"], step["label"] + def test_bacon_results(self, bacon_results): output = practitioner_next_steps(bacon_results, verbose=False) assert len(output["next_steps"]) > 0