feature: thread vcov_type="conley" through SunAbraham + WooldridgeDiD (defer StackedDiD)

Thread Conley (1999) spatial-HAC variance through SunAbraham and WooldridgeDiD-OLS,
mirroring the verified DiD/MPD/TWFE conley path. Pure threading — reuses the existing,
already-conleyreg-validated solve_ols / conley.py machinery; no new variance code.

Both estimators:
- accept vcov_type="conley" + 5 conley_* constructor params (conley_coords,
  conley_cutoff_km, conley_metric, conley_kernel, conley_lag_cutoff) in __init__ +
  get_params/set_params
- call the shared conley._validate_conley_estimator_inputs front-door gate (rejects
  conley + survey_design; checks coords/cutoff/unit/lag/cluster columns) with a literal
  inference="analytical" (neither estimator has a self.inference attr)
- reject conley + n_bootstrap>0 (the multiplier/pairs bootstrap overrides the analytical
  sandwich; the shared validator only knows about wild_bootstrap)
- route conley through the within-transform (FWL) path; build coord/time/unit arrays from
  the post-filter regression frame, row-aligned to the design
- drop the unit auto-cluster on the conley path (an explicit cluster= enables the
  spatial+cluster product kernel)
- carry conley_lag_cutoff on Results + a Conley variance-label line in summary()
  (SunAbrahamResults also gains cluster_name)

WooldridgeDiD conley is OLS-path-only (the method != "ols" guard keeps it off
logit/poisson). Existing SEs stay bit-identical (conley is purely additive).

StackedDiD conley is NOT threaded — deferred for a methodology reason (the stacked design
replicates units across sub-experiments, so Conley would see same-unit copies at distance
0; no conleyreg anchor; paper-gated). The prior "same shape as the SunAbraham follow-up"
framing is corrected in the rejection message, REGISTRY, and TODO.

Tests (tests/test_conley_vcov.py TestConleySunAbraham / TestConleyWooldridge): smoke
(cross-sectional + panel finite SE, label, metadata), FWL-composability (the
within-transform conley SE equals a full-dummy [intercept, treated*post, C(unit), C(time)]
solve_ols conley SE at atol 1e-7 — the primary, non-tautological correctness anchor),
conley+cluster product kernel, rejections (survey / n_bootstrap / weights / malformed
validator inputs / logit), unbalanced-panel alignment, get/set_params round-trip.

Docs: REGISTRY SA + WooldridgeDiD sections flipped to supported + a distinct non-survey
OLS-sandwich entry in the Conley deferral-status note; CHANGELOG Added; TODO rows resolved
+ StackedDiD reclassified; llms-full.txt + param docstrings.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: igerber

CHANGELOG.md

@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - **New estimator: `SyntheticControl` — classic Synthetic Control Method (Abadie, Diamond & Hainmueller 2010; Abadie & Gardeazabal 2003).** Standalone estimator (`diff_diff/synthetic_control.py`) + `SyntheticControlResults` (`diff_diff/synthetic_control_results.py`) + `synthetic_control()` convenience function, exported from `diff_diff`. Builds a single treated unit's counterfactual as a convex combination of never-treated donor units — **donor (unit) weights only**, no time weights or ridge, distinct from `SyntheticDiD`. The inner simplex-constrained weighted-LS solve `W*(V)` reuses `utils._sc_weight_fw` (folding `V^½` into the predictor matrix, `intercept=False`, `zeta=0`); the diagonal predictor-importance matrix `V` is selected data-driven by minimizing pre-period outcome MSPE (`v_method="nested"`, softmax-on-simplex multistart Nelder-Mead + Powell polish) or supplied by the user (`v_method="custom"`). Predictors are built from `predictors`/`predictor_window`/`predictors_op`, `special_predictors`, and per-period outcome lags (`pre_period_outcomes`), in the R `Synth::dataprep` row order; per-row standardization (SD over donors+treated, ddof=1) matches the R `Synth::synth` source. Reports the gap path (`α̂_1t = Y_1t − Σ_j w_j Y_jt`), `att` (mean post-period gap), `pre_rmspe`, donor weights, `v_weights`, and a predictor-balance table. **No analytical standard error** — `se`/`t_stat`/`p_value`/`conf_int` are NaN (in-space placebo permutation inference with the post/pre RMSPE-ratio statistic is planned for a follow-up release; `_placebo_gaps`/`_rmspe_ratio`/`_fit_snapshot` are reserved on the results object). Ten validation gates baked in: predictor-period leakage, absorbing post-period suffix + no-anticipation cross-check against the treatment column, post-period canonicalization, donor-pool filtering before period derivation, empty-window rejection, poor-pre-fit `UserWarning` (RMSPE > SD of treated pre-outcomes), duplicate-predictor-label rejection, inner-solve non-convergence warning, order-independent gap-path rebuild, and the `standardize="none"` deviation; plus fail-closed `custom_v` cross-field rules and degenerate single-donor / single-pre-period handling. **R-`Synth` parity** (`tests/test_methodology_synthetic_control.py`, fixtures generated by `benchmarks/R/generate_synth_basque_golden.R` into `tests/data/`): two-tier on the Basque Country study — Tier-1 feeds R's `solution.v` via `custom_v` and reproduces the published donor weights (region 10 Cataluña 0.851 + region 14 Madrid 0.149) to `atol=1e-3` deterministically; Tier-2 (`@pytest.mark.slow`) checks the data-driven nested fit lands in a tolerance band (the nested `V` legitimately differs because the outer objective uses all pre periods, not R's `time.optimize.ssr` window). Documented in `docs/methodology/REGISTRY.md` §SyntheticControl (with `**Deviation from R:** standardize="none"` and `**Note:**` labels for the standardization formula, objective window, softmax `V` parametrization, and 1×SD poor-fit threshold), `docs/api/synthetic_control.rst`, the LLM guides, and `README.md`.
 - **StaggeredTripleDifference methodology-review-tracker promotion: In Progress → Complete**, plus a new opt-in Eq-4.14 overall ATT. Closes the Ortiz-Villavicencio & Sant'Anna (2025, arXiv:2505.09942v3) primary-source review on the tracker (PR-A #499 added the paper review on file; this PR validates the source against it). New paper-equation-anchored Verified Components in `tests/test_methodology_staggered_triple_diff.py` (Theorem 4.1 / Eq. 4.5 RA=IPW=DR identification; Eq. 4.1 three-term DDD decomposition; Eqs. 4.11-4.12 optimal-GMM weight normalization + single-group reduction; Eq. 4.13 event-study cohort-share weighting; Eq. 4.14 / Cor. 4.2 overall) alongside the existing R cross-validation against `triplediff::ddd(panel=TRUE)` + `agg_ddd()`. **New feature — opt-in `overall_att_es` (paper Eq. 4.14 overall):** the unweighted mean of the post-treatment event-study effects ES(e), exposed on `StaggeredTripleDiffResults` (with `overall_se_es` / `overall_t_stat_es` / `overall_p_value_es` / `overall_conf_int_es`) and populated only when `aggregate="event_study"` / `"all"`. The default `overall_att` is unchanged (the Callaway-Sant'Anna simple post-treatment (g,t) average — the library-wide convention). Its analytical SE is the influence function of that mean (the average of the per-event-time combined IFs, routed through the same survey-aware variance estimator as the per-e effects via a new `_se_from_psi` helper); a multiplier-bootstrap SE replaces it under `n_bootstrap>0`. Computed via a side-channel stash on the shared `CallawaySantAnnaAggregationMixin._aggregate_event_study` (no return-signature change; CallawaySantAnna unaffected), over post-treatment `e >= -anticipation` (the library convention, matching `overall_att`). Cross-validated against R `agg_ddd(type="eventstudy")$overall.att` / `overall.se` (SE matches to ~0.1%). REGISTRY `## StaggeredTripleDifference`: the previously-unlabeled overall-aggregation prose is formalized under a `**Note:**` documenting both overalls, and the duplicate aggregation-weight deviation is consolidated (fixing a `P(G=g)` vs R `P(S=g)` mislabel). `METHODOLOGY_REVIEW.md` row L69 promoted to **Complete** (`Last Review = 2026-05-30`) with a Verified Components / R Comparison Results detail block; priority queue pruned. `docs/references.rst` Ortiz-Villavicencio entry pinned to arXiv:2505.09942v3.
+- **SunAbraham + WooldridgeDiD-OLS `vcov_type="conley"` (Conley 1999 spatial-HAC) threading.** Both estimators now accept `vcov_type="conley"` with the five `conley_*` constructor params (`conley_coords`, `conley_cutoff_km`, `conley_metric`, `conley_kernel`, `conley_lag_cutoff`), reusing the already-`conleyreg`-validated `solve_ols` / `conley.py` machinery — within-period spatial HAC at `conley_lag_cutoff=0`, plus the within-unit Bartlett serial term at `conley_lag_cutoff>0` (the panel-aware path, since `conley_time`/`conley_unit` are always supplied — not pooled cross-sectional), no new variance code. Conley routes through each estimator's within-transform path; the unit auto-cluster is dropped on the conley path (an explicit `cluster=` enables the spatial+cluster product kernel); `survey_design=` / `weights` / `n_bootstrap>0` are rejected, and WooldridgeDiD conley is OLS-path-only (`method ∈ {logit, poisson}` + conley still rejected via the `method != "ols"` guard). `SunAbrahamResults` / `WooldridgeDiDResults` gain a `conley_lag_cutoff` field plus a Conley variance-label line in `summary()` (`SunAbrahamResults` also gains `cluster_name`). FWL-composability — the within-transform conley SE equals the full-dummy conley SE — is pinned in `tests/test_conley_vcov.py` (`TestConleySunAbraham` / `TestConleyWooldridge`). **`StackedDiD` conley remains deferred for a methodology reason** (the stacked design replicates units across sub-experiments, so Conley would see same-unit copies at distance 0; no `conleyreg` anchor; paper-gated) — its prior "same shape as the SunAbraham follow-up" framing is corrected in REGISTRY / TODO / the rejection message.
 - **ConleySpatialHAC methodology-review-tracker promotion: In Progress → Complete.** Closes the Conley (1999) *Journal of Econometrics* 92(1) primary-source review on the methodology-review tracker. The paper review on file at `docs/methodology/papers/conley-1999-review.md` was previously merged (2026-05-09); this PR is the F.L.I.P. consolidation — new `tests/test_methodology_conley.py` with paper-equation-numbered Verified Components walk-through (~1600 LoC; 10 classes; 60 tests, 5 of them `@pytest.mark.slow`). Coverage: Eq. 4.2 cross-sectional sandwich (pairwise-distance specialization; the project's paper review identifies Eq. 4.2 page 18 as the real-valued/pairwise form, with Eq. 3.13 reserved for the lattice-indexed form), Eq. 4.2 HC0 + rank-1 limits, Andrews (1991) HAC lag truncation matching `conleyreg::time_dist.cpp`, haversine convention with Earth radius 6371.01 km, Phase 2 panel block-decomposed sandwich at `atol=1e-12`, sparse k-d-tree dense-vs-sparse bit-identity (Wave A #120 numerical correctness), and R `conleyreg` v0.1.9 parity at `atol=1e-6` on 6 fixtures (3 cross-sectional + 3 panel) plus the sparse-forced and time-asymmetric kernel parity contracts. Three dedicated deviations-area classes: `TestConleyLibraryExtensions` (Wave A library extensions — combined spatial+cluster product kernel #119, callable conley_metric validation #123, sparse k-d-tree activation #120, indefiniteness guard), `TestConleyDeviationsFromR` (1-D radial Bartlett vs paper's 2-D separable Eq. 3.14, time-label normalization via `np.unique`, independent temporal kernel deferred), and `TestConleyDeferrals` (5 fail-closed `NotImplementedError`/`TypeError` contracts: LinearRegression + survey_design, DiD/MPD/TWFE + survey_design, Conley + weights, SyntheticDiD + Conley, wild_bootstrap + Conley). Methodology-anchored tests extracted from `tests/test_conley_vcov.py`: full classes `TestConleyDirectHelper`, `TestConleyReductions`, `TestConleyReductionsAddendum`, `TestConleyParityR`, `TestConleyParitySpacetime`, `TestConleyPanelHelper`, `TestConleySparseRParityForced`; plus methodology-anchored tests from `TestConleyKernels`, `TestConleyDistanceMetrics`, `TestConleySparse`. File drops 4248 → 3113 lines after extraction. Defensive surface preserved: input validation, NaN/inf guards, dispatch-level validity, estimator-level integration smoke tests, set_params atomicity, sparse-path activation thresholds + density-gate fallback. `METHODOLOGY_REVIEW.md` row L91 promoted to **Complete** with `Last Review = 2026-05-26`; detail block rewritten with Verified Components / Test Coverage / R Comparison Results inline table / Corrections Made / Deviations / Outstanding Concerns. Priority queue at L1386 pruned: PreTrendsPower removed (already Complete since 2026-05-19) and ConleySpatialHAC removed (this PR); substantive-review-blocked renumbered #2-#5 → #1-#4 and consolidation-pass-blocked renumbered #6-#8 → #5-#6.
 
 ### Added / Changed

TODO.md

@@ -103,9 +103,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | WooldridgeDiD: optional *efficiency hint* (NOT a canonical-link violation per W2023 Prop 3.1) when method/outcome pairing is sub-optimal — e.g., `method="ols"` on binary data is consistent under QMLE, but `method="logit"` is typically more efficient. The original framing in this row as a "canonical link requirement" tied to Prop 3.1 was incorrect: Wooldridge (2023) Table 1 lists Gaussian/OLS for "any response" and logistic-Bernoulli for "binary OR fractional". A useful hint exists (efficiency), but should not be framed as a methodology violation. See PR #453 R1 review for the corrected reading. | `wooldridge.py` | #216 | Low |
 | WooldridgeDiD: Stata `jwdid` golden value tests — add R/Stata reference script and `TestReferenceValues` class. | `tests/test_wooldridge.py` | #216 | Medium |
 | PreTrendsPower: CS/SA `anticipation=1` R-parity fixture. The PR-C R-parity goldens cover NIS power + γ_p MDV at `atol=1e-4` on four shifted-grid / regular / irregular / K=1 fixtures, but R `pretrends` has no anticipation parameter so the Python-side `_extract_pre_period_params` anticipation filter (`if t < _pre_cutoff` in `pretrends.py` lines 1138-1150 for CS; mirror in SA branch) is not R-parity-locked. Build a synthetic `CallawaySantAnnaResults` (or `SunAbrahamResults`) with `anticipation=1` and a t=-1 event-study entry that should be filtered before reaching `_compute_power_nis`, then assert the resulting γ_p matches R's `slope_for_power()` on the K=4 shifted-grid fixture. Existing PR-B MC-based tests (`TestPretrendsPropositions`) and full-VCV tests (`TestPretrendsCovarianceSource`) already cover the filter mechanically; this would close the loop against R. | `tests/test_methodology_pretrends.py::TestPretrendsParityR`, `benchmarks/R/generate_pretrends_golden.R` | PR-C follow-up | Low |
-| Extend `SunAbraham` with `vcov_type="conley"` (Conley spatial-HAC) as a first-class feature: thread `conley_coords` / `conley_cutoff_km` / `conley_metric` / `conley_kernel` / `conley_time` / `conley_unit` / `conley_lag_cutoff` through `_fit_saturated_regression`. Phase 1b PR 1/8 deferred this; SA currently rejects `vcov_type="conley"` at `__init__` with a deferral message. | `diff_diff/sun_abraham.py` | follow-up | Medium |
-| Extend `StackedDiD` with `vcov_type="conley"` (Conley spatial-HAC) — thread the six `conley_*` params through `solve_ols` at `stacked_did.py:419` (and the `_refit_stacked` closure at `:444`). Phase 1b PR 2/8 deferred this; StackedDiD currently rejects `vcov_type="conley"` at `__init__` with a deferral message. Same shape as the SunAbraham conley follow-up. | `diff_diff/stacked_did.py` | follow-up | Medium |
-| Extend `WooldridgeDiD` with `vcov_type="conley"` — thread the six `conley_*` params through `solve_ols` in `_fit_ols`. Phase 1b PR 3/8 deferred this; WooldridgeDiD currently rejects `vcov_type="conley"` at `__init__` with a deferral message. Same shape as the SunAbraham / StackedDiD conley follow-ups. | `diff_diff/wooldridge.py` | follow-up | Medium |
+| `StackedDiD` `vcov_type="conley"` — deferred for a **methodology** reason, NOT plumbing (unlike the now-shipped SunAbraham / WooldridgeDiD-OLS conley threading): the stacked design replicates each control unit across every sub-experiment it qualifies for (`_build_sub_experiment`), so one geographic unit occupies many stacked rows. Conley's pairwise distance matrix would see those same-unit copies at distance 0 (`K(0)=1`, perfectly correlated), conflating the stacking-replication device with real spatial correlation, and there is no `conleyreg` analogue for stacked DiD to anchor parity. A correct treatment needs a per-stack spatial identifier and is **paper-gated**. | `diff_diff/stacked_did.py` | follow-up | Low |
 | Extend `WooldridgeDiD` `method ∈ {"logit","poisson"}` paths with `vcov_type ∈ {classical, hc2, hc2_bm}`. The GLM QMLE sandwich uses pseudo-residuals (`weights=p(1-p)` for logit, `weights=μ_i` for Poisson, aweight semantics); composing HC2 leverage and Bell-McCaffrey Satterthwaite DOF with QMLE on canonical-link pseudo-residuals needs derivation + R parity against `clubSandwich::vcovCR(glm(...), type="CR2")`. Phase 1b PR 3/8 rejects `method != "ols" + vcov_type != "hc1"` at `__init__` with a deferral pointer here. | `diff_diff/wooldridge.py` (`_fit_logit`, `_fit_poisson`) | follow-up | Medium |
 | Extend `CallawaySantAnna` with `vcov_type="conley"` — would require deriving a spatial-HAC composition for per-unit influence functions (Conley 1999 spatial kernel × per-(g,t) IF aggregation); no reference implementation exists today. Phase 1b interstitial PR rejected this at `__init__` with a deferral pointer here. | `diff_diff/staggered.py` | follow-up | Low |
 | Extend `TripleDifference` with `vcov_type="conley"` — would require deriving a spatial-HAC composition for the 3-pairwise-DiD influence-function decomposition (Conley 1999 spatial kernel × `inf = w3·IF_3 + w2·IF_2 - w1·IF_1` aggregation); no reference implementation exists today. Phase 1b interstitial #2 PR rejected this at `__init__` with a deferral pointer here. | `diff_diff/triple_diff.py` | follow-up | Low |
@@ -231,7 +229,7 @@ Ordered paydown view across the tables above. Tier A → D is by effort × risk,
 
 ### Standard Error Consistency
 
-`vcov_type` has subsumed the previously-proposed `se_type` knob. `DifferenceInDifferences` and `TwoWayFixedEffects` accept `vcov_type ∈ {"classical", "hc1", "hc2", "hc2_bm", "conley"}` (the validated set in `linalg.py::_VALID_VCOV_TYPES`); cluster-robust variance is obtained by passing `cluster=` alongside the heteroscedasticity kind (`hc1 + cluster` ⇒ CR1 Liang-Zeger; `hc2_bm + cluster` ⇒ CR2 Bell-McCaffrey, including the weighted path landed via the clubSandwich WLS-CR2 port; the N>1 absorbed-FE + weights composition remains gated by the open multi-absorb row in the table above); wild cluster bootstrap is a separate `inference="wild_bootstrap"` path on the same estimator. Threading `vcov_type` through the 8 standalone estimators (`CallawaySantAnna`, `SunAbraham`, `ImputationDiD`, `TwoStageDiD`, `TripleDifference`, `StackedDiD`, `WooldridgeDiD`, `EfficientDiD`) is **complete** as of Phase 1b; four of them (`CallawaySantAnna`, `TripleDifference`, `ImputationDiD`, `EfficientDiD`) are permanently narrow to `{"hc1"}` per their influence-function variance, and `TwoStageDiD` is likewise narrow because its Gardner GMM-corrected meat has no single cross-stage hat matrix for `classical`/`hc2`/`hc2_bm`; the remaining per-estimator `vcov_type="conley"` extensions are tracked as follow-up rows in the table above.
+`vcov_type` has subsumed the previously-proposed `se_type` knob. `DifferenceInDifferences` and `TwoWayFixedEffects` accept `vcov_type ∈ {"classical", "hc1", "hc2", "hc2_bm", "conley"}` (the validated set in `linalg.py::_VALID_VCOV_TYPES`); cluster-robust variance is obtained by passing `cluster=` alongside the heteroscedasticity kind (`hc1 + cluster` ⇒ CR1 Liang-Zeger; `hc2_bm + cluster` ⇒ CR2 Bell-McCaffrey, including the weighted path landed via the clubSandwich WLS-CR2 port; the N>1 absorbed-FE + weights composition remains gated by the open multi-absorb row in the table above); wild cluster bootstrap is a separate `inference="wild_bootstrap"` path on the same estimator. Threading `vcov_type` through the 8 standalone estimators (`CallawaySantAnna`, `SunAbraham`, `ImputationDiD`, `TwoStageDiD`, `TripleDifference`, `StackedDiD`, `WooldridgeDiD`, `EfficientDiD`) is **complete** as of Phase 1b; four of them (`CallawaySantAnna`, `TripleDifference`, `ImputationDiD`, `EfficientDiD`) are permanently narrow to `{"hc1"}` per their influence-function variance, and `TwoStageDiD` is likewise narrow because its Gardner GMM-corrected meat has no single cross-stage hat matrix for `classical`/`hc2`/`hc2_bm`. The per-estimator `vcov_type="conley"` extensions are tracked as follow-up rows in the table above: SunAbraham + WooldridgeDiD-OLS are **shipped** (within-transform conley via `solve_ols`); StackedDiD is deferred for a methodology reason (unit replication × spatial distance); the IF-based / GMM estimators have no reference implementation.
 
 ### Type Annotations
 

diff_diff/conley.py

@@ -212,7 +212,7 @@ def _validate_conley_estimator_inputs(
 ) -> None:
     """Shared front-door validation for ``vcov_type='conley'`` on the
     estimator entry points (``DifferenceInDifferences``, ``MultiPeriodDiD``,
-    ``TwoWayFixedEffects``).
+    ``TwoWayFixedEffects``, ``SunAbraham``, and ``WooldridgeDiD``-OLS).
 
     Each estimator's ``fit()`` calls this BEFORE building Conley arrays or
     threading them into the variance computation. The eight checks below