Compose by_path / paths_of_interest with survey_design (Wave 4 #10)

CHANGELOG.md

@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`ChaisemartinDHaultfoeuille.by_path` and `paths_of_interest` now compose with `survey_design`** for analytical Binder TSL SE and replicate-weight bootstrap variance. The `NotImplementedError` gate at `chaisemartin_dhaultfoeuille.py:1233-1239` is replaced by a per-path multiplier-bootstrap-only gate (`survey_design + n_bootstrap > 0` under by_path / paths_of_interest still raises, since the survey-aware perturbation pivot for path-restricted IFs is methodologically underived). Per-path SE routes through the existing `_survey_se_from_group_if` cell-period allocator: the per-period IF (`U_pp_l_path`) is built with non-path switcher-side contributions skipped (control contributions are unchanged, matching the joiners/leavers IF convention; preserves the row-sum identity `U_pp.sum(axis=1) == U`), cohort-recentered via `_cohort_recenter_per_period`, then expanded to observations as `psi_i = U_pp[g_i, t_i] · (w_i / W_{g_i, t_i})`. Replicate-weight designs unconditionally use the cell allocator (Class A contract from PR #323). New `_refresh_path_inference` helper post-call refreshes `safe_inference` on every populated entry across `multi_horizon_inference`, `placebo_horizon_inference`, `path_effects`, and `path_placebos` so all four surfaces use the same final `df_survey` after per-path replicate fits append `n_valid` to the shared accumulator. Path-enumeration ranking under `survey_design` remains unweighted (group-cardinality, not population-weight mass). Lonely-PSU policy stays sample-wide, not per-path. Telescope invariant: on a single-path panel, per-path SE matches the global non-by_path survey SE bit-exactly. **No R parity** — R `did_multiplegt_dyn` does not support survey weighting; this is a Python-only methodology extension. The global non-by_path TSL multiplier-bootstrap path is unaffected (anti-regression test `tests/test_chaisemartin_dhaultfoeuille.py::TestByPathSurveyDesignAnalytical::test_global_survey_plus_n_bootstrap_still_works` locks the per-path-only scope of the new gate). Cross-surface invariants regression-tested at `TestByPathSurveyDesignAnalytical` (~17 tests across gate / dispatch / analytical SE / replicate-weight SE / per-path placebos / `trends_linear` composition / unobserved-path warnings / final-df refresh regressions) and `TestByPathSurveyDesignTelescope`. See `docs/methodology/REGISTRY.md` §`ChaisemartinDHaultfoeuille` `Note (Phase 3 by_path ...)` → "Per-path survey-design SE" for the full contract.
 - **Inference-field aliases on staggered result classes** for adapter / external-consumer compatibility. Read-only `@property` aliases expose the flat `att` / `se` / `conf_int` / `p_value` / `t_stat` names (matching `DiDResults` / `TROPResults` / `SyntheticDiDResults` / `HeterogeneousAdoptionDiDResults`) on every result class that previously only carried prefixed canonical fields: `CallawaySantAnnaResults`, `StackedDiDResults`, `EfficientDiDResults`, `ChaisemartinDHaultfoeuilleResults`, `StaggeredTripleDiffResults`, `WooldridgeDiDResults`, `SunAbrahamResults`, `ImputationDiDResults`, `TwoStageDiDResults` (mapping to `overall_*`); `ContinuousDiDResults` (mapping to `overall_att_*`, ATT-side as the headline, ACRT-side accessible unchanged via `overall_acrt_*`); `MultiPeriodDiDResults` (mapping to `avg_*`). `ContinuousDiDResults` additionally exposes `overall_se` / `overall_conf_int` / `overall_p_value` / `overall_t_stat` aliases for naming consistency with the rest of the staggered family. Aliases are pure read-throughs over the canonical fields — no recomputation, no behavior change — so the `safe_inference()` joint-NaN contract (per CLAUDE.md "Inference computation") is inherited automatically (NaN canonical → NaN alias, locked at `tests/test_result_aliases.py::test_pattern_b_aliases_propagate_nan`). The native `overall_*` / `overall_att_*` / `avg_*` fields remain canonical for documentation and computation. Motivated by the `balance.interop.diff_diff.as_balance_diagnostic()` adapter (`facebookresearch/balance` PR #465) which calls `getattr(res, "se", None)` / `getattr(res, "conf_int", None)` without a fallback chain — pre-alias, every staggered result class returned `None` on those keys, silently dropping `se` and `conf_int` from the adapter's diagnostic dict. 23 alias-mechanic + balance-adapter regression tests at `tests/test_result_aliases.py`. Patch-level (additive on stable surfaces).
 - **`ChaisemartinDHaultfoeuille.by_path` + non-binary integer treatment** — `by_path=k` now accepts integer-coded discrete treatment (D in Z, e.g. ordinal `{0, 1, 2}`); path tuples become integer-state tuples like `(0, 2, 2, 2)`. The previous `NotImplementedError` gate at `chaisemartin_dhaultfoeuille.py:1870` is replaced by a `ValueError` for continuous D (e.g. `D=1.5`) at fit-time per the no-silent-failures contract — the existing `int(round(float(v)))` cast in `_enumerate_treatment_paths` is now defensive (no-op for integer-coded D). Validated against R `did_multiplegt_dyn(..., by_path)` for D in `{0, 1, 2}` via the new `multi_path_reversible_by_path_non_binary` golden-value scenario (78 switchers, 3 paths, single-baseline custom DGP, F_g >= 4): per-path point estimates match R bit-exactly (rtol ~1e-9 on event horizons; rtol+atol envelope for placebo near-zero values), per-path SE inherits the documented cross-path cohort-sharing deviation (~5% rtol observed; SE_RTOL=0.15 envelope). **Deviation from R for D >= 10:** R's `did_multiplegt_by_path` derives the per-path baseline via `path_index$baseline_XX <- substr(path_index$path, 1, 1)`, which captures only the first character of the comma-separated path string (e.g. for `path = "12,12,..."` it captures `"1"` instead of `"12"`); this mis-allocates R's per-path control-pool subset for D >= 10. Python's tuple-key matching is correct in this regime — the per-path point estimates we compute are correct; R's per-path subset for the same path is buggy. The shipped parity scenario stays in `D in {0, 1, 2}` to avoid the R bug. R-parity test at `tests/test_chaisemartin_dhaultfoeuille_parity.py::TestDCDHDynRParityByPathNonBinary`; cross-surface invariants regression-tested at `tests/test_chaisemartin_dhaultfoeuille.py::TestByPathNonBinary`.
 - **New `paths_of_interest` kwarg on `ChaisemartinDHaultfoeuille`** for user-specified treatment-path subsets, alternative to `by_path=k`'s top-k automatic ranking. Mutually exclusive with `by_path`; setting both raises `ValueError` at `__init__` and `set_params` time. Each path tuple must be a list/tuple of `int` of length `L_max + 1` (uniformity validated at `__init__`; length match against `L_max + 1` validated at fit-time); `bool` and `np.bool_` are explicitly rejected, `np.integer` accepted and canonicalized to Python `int` for tuple-key consistency. Duplicates emit a `UserWarning` and are deduplicated; paths not observed in the panel emit a `UserWarning` and are omitted from `path_effects`. Paths appear in `results.path_effects` in the user-specified order, modulo deduplication and unobserved-path filtering. Composes with non-binary D and all downstream `by_path` surfaces (bootstrap, per-path placebos, per-path joint sup-t bands, `controls`, `trends_linear`, `trends_nonparam`) — mechanical filter on observed paths via the same `_enumerate_treatment_paths` call site, no methodology change. **Python-only API extension; no R equivalent** — R's `did_multiplegt_dyn(..., by_path=k)` only accepts a positive int (top-k) or `-1` (all paths). The `by_path` precondition gate at `chaisemartin_dhaultfoeuille.py:1118` (drop_larger_lower / L_max / `heterogeneity` / `design2` / `honest_did` / `survey_design` mutex) and the 11 `self.by_path is not None` activation branches in `fit()` were rerouted to fire under either selector. Validation + behavior + cross-feature regressions at `tests/test_chaisemartin_dhaultfoeuille.py::TestPathsOfInterest`.