ContinuousDiD methodology-review-tracker promotion: In Progress -> Complete

CHANGELOG.md

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+- **ContinuousDiD methodology-review-tracker promotion.** Tracker row flipped **In Progress** → **Complete** with full Verified Components / Test Coverage / Corrections Made / Deviations / Outstanding Concerns structure mirroring the HAD precedent (PR #473). REGISTRY `## ContinuousDiD` gains a formal Deviations block consolidating the boundary-knots deviation from R `contdid` v0.1.0 (`range(dose)` vs `range(dvals)` — library avoids extrapolation), the `bspline_derivative` derivative-failure `UserWarning` (Phase 2 axis-C #12), the `+inf` → `0` never-treated recoding warning, and the zero-`first_treat`+nonzero-`dose` force-zeroing warning (both axis-E silent-coercion fixes) into a single AI-review-recognized labeled surface. R cross-language coverage for ContinuousDiD runs at relative tolerance across two surfaces: (a) **scalar parity with raw R `cont_did` / `pte_default`** at 1% on overall ATT for all 6 benchmarks and on overall ACRT for benchmarks 4-5 (benchmark 6 is event-study, scalar `overall_att` only); (b) **harmonized boundary-knot-normalized curve parity** with R-side ATT(d) / ACRT(d) reconstructed under `Boundary.knots = range(treated_doses)` (matching the library) on benchmarks 1-3 via the benchmark harness — `_run_r_contdid` does the R-side rebuild at `tests/test_methodology_continuous_did.py:333-367`, and `_compare_with_r` orchestrates the Python-vs-R comparison at `:395-459` — max ATT(d) at 1% and max ACRT(d) at 2%. NOT bit-exact (`atol=1e-8`) like HAD — the boundary-knots deviation precludes algorithmic bit-equality on aggregated dose-response curves. Surface (a) is direct raw-package parity; surface (b) is reconstructed-basis parity because raw `contdid` curves use `range(dvals)`. No source code changes, no new tests, no new docstrings — consolidation only against the existing 15 methodology tests (`tests/test_methodology_continuous_did.py`), 80 unit tests (`tests/test_continuous_did.py`), and `docs/methodology/continuous-did.md` theory note. `METHODOLOGY_REVIEW.md` ContinuousDiD row promoted **In Progress** → **Complete**.
 - **`SpilloverDiD(vcov_type="conley", survey_design=...)` integration via stratified-Conley sandwich on PSU totals (Wave E.2).** Lifts the Wave E.1 `NotImplementedError` (`spillover.py:2201` upfront, `two_stage.py:217` helper-level) and adds spatial-HAC + design-based variance for the previously deferred composition. **Documented synthesis** of Conley (1999) spatial-HAC × Gerber (2026, arXiv:2605.04124) Proposition 1 Binder TSL (the Wave E.1 foundation) × Wave D Gardner GMM first-stage uncertainty correction (Butts 2021 §3.1 + Gardner 2022 §4) applied to SpilloverDiD's ring-indicator stage-2 design. No reference software combines all three ingredients on a two-stage influence function. **Mechanical composition (panel-aware):** preserves the library's existing `conley_lag_cutoff = 0` semantic at `diff_diff.conley._compute_conley_meat` ("within-period spatial only — exclude cross-period spatial pairs") by looping over periods. For each period `t`, SpilloverDiD's per-obs Hájek-weighted Wave D IF `psi_i` is aggregated to per-period PSU totals `S_psu_t[g] = sum_{i in PSU g, time t} psi_i` (via `np.add.at`); per-PSU spatial centroids are panel-constant (mean of per-observation `conley_coords` within each PSU, vectorized `np.add.at` sums / `np.bincount` counts); for each stratum the within-stratum sandwich is `M_h_t = (1 - f_h) * n_h/(n_h-1) * sum_{j,k in PSUs_h} K(d(centroid_j, centroid_k) / conley_cutoff_km) * (S_psu_t[j] - S_bar_h_t)(S_psu_t[k] - S_bar_h_t)'`, where K is the Bartlett kernel (SpilloverDiD currently exposes Bartlett only and hardcodes it; the survey helper accepts `"uniform"` too but exposing that on the SpilloverDiD constructor is a separate follow-up) and `d` is haversine / euclidean / callable per `ConleyMetric`. Cross-stratum kernel weights are exactly zero by sampling design (strata are independence partitions). Total meat is `sum_t sum_h M_h_t`. Cross-period spatial pairs are excluded by construction — the per-period loop matches the library's panel Conley contract exactly. **Reduction semantics (load-bearing for tests):** the orchestrator's panel-aware meat equals `sum_t` of per-period within-stratum stratified-Conley sandwiches on per-period PSU totals (pinned at `tests/test_spillover.py::TestSpilloverDiDWaveE2ConleySurveyDesign::test_b_panel_aware_per_period_sum_invariant`); single stratum (H = 1, FPC = inf) reduces to `sum_t` plain Conley sandwich on per-period PSU totals (NOT on time-collapsed totals). **Implementation:** new `_compute_stratified_conley_meat_from_psu_scores` helper in `diff_diff/survey.py` (parallel to existing `_compute_stratified_meat_from_psu_scores` 3-tuple `(meat, variance_computed, legitimate_zero_count)` contract; per-stratum loop replaces the inner `centered.T @ centered` with `_compute_conley_meat(scores=centered, coords=psu_coords_h, ...)` in cross-sectional mode); new dispatch wrapper `_compute_stratified_conley_meat` in `diff_diff/two_stage.py` (parallel to existing `_compute_binder_tsl_meat`, performs per-obs Psi → PSU aggregation + centroid derivation + dispatch to survey helper, intentionally drops `cluster_ids` at the dispatch boundary — see Restrictions). `_compute_gmm_corrected_meat` conley branch extended with `if resolved_survey is not None` routing to the new wrapper; the `resolved_survey is None` branch is bit-identical to Wave D. **Singleton-stratum `lonely_psu="adjust"` parity:** the survey helper mirrors the Binder helper's `continue` to skip the FPC scale on singleton strata (with `n_h = 1` the scale `n_h / (n_h - 1)` would divide by zero); the degenerate one-PSU kernel `K = [[K(0)]] = [[1.0]]` reduces to `centered.T @ centered`, matching Binder's singleton-adjust output. **Saturated `df_survey = 0` NaN-fail:** mirrors Wave E.1 (`_compute_stratified_conley_meat` returns NaN meat with `UserWarning` template "Wave E.2 stratified-Conley sandwich: df_survey = 0..." so callers can `pytest.warns(UserWarning, match="Wave E.2 stratified-Conley")`). **Public surface restrictions:** replicate-weight variance (BRR / Fay / JK1 / JKn / SDR) raises `NotImplementedError` (inherits Wave E.1 gate; per-replicate full refit is separate follow-up scope); `cluster=<col> + survey_design.psu + vcov_type="conley"` coerces `cluster=<col>` to PSU per Wave E.1's warn-and-use-PSU pattern (the Conley cluster product kernel becomes a no-op after PSU aggregation, so `cluster_ids` is intentionally not threaded into the inner Conley kernel call — every PSU is its own cluster post-aggregation, which would zero all cross-PSU pairs); LinearRegression-side `vcov_type="conley" + survey_design=` gate at `diff_diff/linalg.py:2853` remains (separate Bertanha-Imbens 2014 weighted-Conley "Phase 5" roadmap, not Wave E); DiagnosticReport routing for `SpilloverDiDResults(vcov_type="conley", survey_design=)` requires `_APPLICABILITY` / `_PT_METHOD` registration (separate Wave F PR). **Tests:** new `TestSpilloverDiDWaveE2ConleySurveyDesign` and `TestSpilloverDiDWaveE2ConleySurveyDesignEventStudy` classes in `tests/test_spillover.py` (bit-identical no-survey fallback; panel-aware per-period sum invariant on the orchestrator + helper composition; hand-computation methodology anchor; single-stratum ≡ plain Conley on PSU totals; cross-stratum independence as a unit test on the survey helper with interleaved cross-stratum centroids; Binder vs Conley singleton-adjust FPC skip parity; lonely-PSU sensitivity across three modes; FPC large ≡ no-FPC and FPC = n_h zeros stratum; saturated NaN-fail with `pytest.warns(match="Wave E.2 stratified-Conley")`; replicate-weight + non-pweight rejections; cluster warn-and-use-PSU; fit idempotency; `finite_mask` survey-array subsetting; no-PSU coverage — weights-only `SurveyDesign(weights=...)`, strata-only `SurveyDesign(weights=..., strata=...)`, and a per-period re-index unit invariant pinning that no cross-period spatial pairs leak into the meat on implicit-PSU layouts; event-study path on both `is_staggered=True`/`False` branches per `feedback_cohort_loop_trigger_cache_both_branches`; drift goldens at `rtol=1e-12 / atol=1e-14`). The pre-existing `tests/test_spillover.py::test_fit_conley_plus_survey_design_not_implemented` Wave E.1-era gate-assertion test is removed (replaced by the positive-path tests above). Wave E.1 entry's "Public surface restrictions" bullet updated to past-tense the conley+survey gate reference.
 - **HeterogeneousAdoptionDiD methodology-review-tracker promotion.** New `tests/test_methodology_had.py` (6 classes, 36 tests) with paper-equation-numbered Verified Components walk-through against de Chaisemartin, Ciccia, D'Haultfœuille & Knau (2026) arXiv:2405.04465v6 (Equations 3 / 7 / 11 / 18 / 29 and Theorems 1 / 3 / 4 / 7): Design 1' MC recovery on both the zero-boundary DGP AND a nonzero-boundary-intercept DGP (`ΔY = c + β·D + ε` with `c != 0`) so the `att = (mean(ΔY) − τ_bc) / mean(D)` subtraction term is verified explicitly, N(0,1) coverage at `n_replicates=200`, mass-point Wald-IV closed-form equivalence at `atol=1e-9`, QUG limit-law distributional match at KS-stat ≤ 0.05 (n_draws=5000), Yatchew-HR paper-literal `σ²_diff = 1/(2G)` normalization lock, joint Stute pre-trends + homogeneity H0 fail-to-reject on both surfaces and H1 reject for joint homogeneity under a nonlinear DGP, and library-deviation locks (equal-weighting via selective low-dose-region replication, sup-t bootstrap gating, staggered-timing fail-closed `ValueError`). Added "Non-testable assumptions (paper Section 3.1.2)" Notes block to `HeterogeneousAdoptionDiD` class docstring + "Scope (what this test does NOT cover)" clauses to `qug_test` / `stute_test` / `yatchew_hr_test` / `did_had_pretest_workflow` Notes sections explicitly stating that the pre-tests verify ADJACENT assumptions (Assumption 4 / 7 / 8) and CANNOT test Assumptions 5 or 6. Phase-4 validation-harness items (Pierce-Schott 2016 Figure 2 replication, Table 1 coverage-rate reproduction across 3 DGPs × G ∈ {100, 500, 2500}) waived with documented rationale: R parity at `atol=1e-8` in `tests/test_did_had_parity.py` (3 DGPs × 5 method combos, bit-exact via `rtol=0`) is a strictly stronger anchor than coverage-rate Monte Carlo, and the paper itself self-acknowledges (Section 5.2) that NP estimators are too noisy to be informative on the LBD-restricted PNTR panel. REGISTRY HAD section gains a consolidated Deviations block (5 entries with framing header) and closes 2 of 3 unchecked Implementation Checklist items — the staggered-timing fail-closed `ValueError` and the Assumption 5/6 non-testability documentation; the `covariates=` Theorem 6 follow-up and the extensive-margin / "consider running standard DiD" warning both remain explicitly tracked in `TODO.md` as Low-priority follow-ups rather than claimed-closed. `dechaisemartin-2026-review.md:182-194` requirements checklist boxes the Phase 1a/1b/1c implementation-status closures + the Assumption 5/6 documentation + the staggered-timing closures; the extensive-margin item is acknowledged as partial (zero-dose `UserWarning` exists in `qug_test`; main-`fit()` "consider standard DiD" recommendation is the TODO follow-up). `METHODOLOGY_REVIEW.md` HAD row promoted **In Progress** → **Complete**.
 - **SunAbraham `vcov_type` parameter (Phase 1b PR 1/8).** `SunAbraham(vcov_type=...)` now accepts `{"classical","hc1","hc2","hc2_bm"}` (defaults to `"hc1"`, which preserves prior behavior bit-equally - SA historically hard-coded HC1). Auto-cluster-at-unit dropped when the user opts into explicit `vcov_type="hc2"` or `vcov_type="classical"` (one-way only); preserved for `"hc1"` and `"hc2_bm"`. When `vcov_type in {"classical","hc2","hc2_bm"}`, `_fit_saturated_regression` auto-routes to a full-dummy saturated design (mirrors TWFE Gate 1 from PR #469): FWL preserves cohort coefficients but not the hat matrix, so HC2 leverage and Bell-McCaffrey Satterthwaite DOF must be computed on the full FE projection. Empirically matches R `lm()` summary classical SE, `sandwich::vcovHC(type="HC2")`, and `clubSandwich::vcovCR(..., type="CR2")` + `coef_test()$df_Satt` at atol=1e-10 (cohort SE and BM DOF pinned in `tests/test_methodology_sun_abraham.py`). For `vcov_type="hc2_bm"`, the user-facing aggregated inference (`event_study_effects[e]['p_value']`/`['conf_int']`, `overall_p_value`/`overall_conf_int`) uses CR2 Bell-McCaffrey contrast DOF — matches `clubSandwich::Wald_test(test="HTZ")$df_denom` at atol=1e-10 (mirrors PR #465's `_compute_cr2_bm_contrast_dof` pattern for MultiPeriodDiD's post-period-average ATT). `vcov_type` is now propagated to `SunAbrahamResults.vcov_type` for downstream introspection. `SurveyDesign` (any kind — analytical weights, stratified, PSU, or replicate-weight) combined with `vcov_type in {"classical","hc2","hc2_bm"}` raises `NotImplementedError`: the survey-design TSL (or replicate-weight refit) variance overrides the analytical sandwich family, and the auto-cluster guard for one-way families would silently downgrade unit-level PSUs to per-observation PSUs. Use `vcov_type="hc1"` (default) for survey designs. `conley` rejected at `__init__` with a deferral message (would require threading 6+ `conley_*` params through the saturated regression call). **Deviation from R:** SA's within-transform HC1 SE differs from `fixest::sunab()` by ~1-2% (~2e-3 absolute) on typical panel sizes due to a different `(n-k)` finite-sample correction (fixest counts absorbed FE in k_total; SA's `solve_ols` counts only within-transformed columns); the IW aggregation step is otherwise identical (pinned at atol=5e-3, tracked in TODO.md). First PR of the Phase 1b standalone-estimator threading initiative (7 PRs to follow: StackedDiD, WooldridgeDiD-OLS, CallawaySantAnna, ImputationDiD, TripleDifference, TwoStageDiD, EfficientDiD).