Reject Conley on panel estimators; remove dead conley result-class wiring

Address CI Codex review of PR #411 (P1#1 + P1#2):

P1#1 — Panel estimators with vcov_type="conley" silently produced wrong
SE because cross-sectional Conley over (unit, time) rows treated same-
unit cross-time pairs as d_ij=0 -> K=1, mishandling the space-time HAC.
Phase 1 supports cross-sectional Conley only; reject panel fits at
fit-time on DifferenceInDifferences, TwoWayFixedEffects, and
MultiPeriodDiD with NotImplementedError. Practitioners pre-collapse to
per-unit first-differences and call compute_robust_vcov directly. Phase
2 will add the space-time product kernel (Driscoll-Kraay) and lift the
rejection. Granular Conley-arg validation collapsed into the single
unconditional reject (cluster/absorb/coords/cutoff combinations all
hit the same path).

P1#2 — conley_metric was dropped at the result boundary and
_format_vcov_label hard-coded "km" for the cutoff label even when
metric was "euclidean". With panels rejected, the conley_cutoff_km /
conley_kernel fields on DiDResults / MultiPeriodDiDResults are now
unreachable; remove the dead fields, the dead arg passes from
estimators.py / twfe.py, and the dead "conley" branch in
_format_vcov_label.

Tests added: TWFE / DiD / MPD panel-rejection regressions, including a
repeated-coords-across-periods regression per the CI reviewer's
recommendation. 70 Conley tests + 401 targeted regression tests pass.

REGISTRY / CHANGELOG / llms.txt / README / TODO updated to reflect
that the only supported Phase 1 Conley path is direct
LinearRegression / compute_robust_vcov on a single-period design.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

CHANGELOG.md

@@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
-- **Conley (1999) spatial-HAC standard errors via `vcov_type="conley"`** on `DifferenceInDifferences`, `TwoWayFixedEffects`, and `MultiPeriodDiD` (Phase 1 of the spillover-conley initiative). New keyword-only kwargs on `__init__`: `conley_coords=(<lat_col>, <lon_col>)` (column-name tuple from `data`), `conley_cutoff_km=<float>` (positive finite bandwidth in km for haversine, or coord units for euclidean — REQUIRED, no default per the no-silent-failures contract), `conley_metric="haversine"|"euclidean"|callable` (default `"haversine"`; great-circle uses Earth's mean radius 6371.01 km matching R `conleyreg`), `conley_kernel="bartlett"|"uniform"` (default `"bartlett"` is PSD-guaranteed; `"uniform"` emits `UserWarning` if the meat has a materially negative eigenvalue per Conley 1999 footnote 11). Variance estimator `Var̂(β) = (X'X)^{-1} · ( Σ_{i,j} K(d_ij/h) · X_i ε_i ε_j X_j' ) · (X'X)^{-1}` (Conley 1999 Eq 4.2). FWL composes cleanly because the meat depends only on scores `X·ε`, both of which within-transformation preserves — `TwoWayFixedEffects(vcov_type="conley", ...)` is supported, UNLIKE `hc2`/`hc2_bm` which need the full hat matrix. TWFE auto-cluster-at-unit is disabled when `vcov_type="conley"`; explicit `cluster=` raises `NotImplementedError` (combined product kernel deferred to Phase 2). `n > 20_000` emits a `UserWarning` about the dense O(n²) distance-matrix memory; sparse k-d-tree fast path is queued for Phase 2. `SyntheticDiD(vcov_type="conley")` raises `TypeError` (uses bootstrap variance, not analytical sandwich); `set_params` mirrors the constructor rejection. `vcov_type="conley"` + `weights=` / `survey_design=` / `absorb=` raises `NotImplementedError` (Bertanha-Imbens 2014 weighted-Conley + arbitrary FE projection are deferred to follow-up phases). `TwoWayFixedEffects(vcov_type="conley", inference="wild_bootstrap")` raises `NotImplementedError` (Conley analytical spatial-HAC and wild cluster bootstrap are different inference paths). Helpers live in new module `diff_diff/conley.py` (`_haversine_km`, `_pairwise_distance_matrix`, `_bartlett_kernel`, `_uniform_kernel`, `_validate_conley_kwargs`, `_compute_conley_vcov`); `compute_robust_vcov` in `diff_diff/linalg.py` imports the dispatch helpers. R `conleyreg` parity (Düsterhöft 2021, CRAN v0.1.9) on three benchmark fixtures (`benchmarks/data/r_conleyreg_conley_golden.json`, regenerable via `benchmarks/R/generate_conley_golden.R`); observed max abs diff 5.7e-16. Earth radius 6371.01 km matches `conleyreg::haversine_dist`. Test file `tests/test_conley_vcov.py` skips parity cleanly when the JSON is absent. `result.summary()` prints `"Conley spatial HAC (bartlett, cutoff=200.0km)"` via the extended `_format_vcov_label` helper. New REGISTRY section `## ConleySpatialHAC`. Tracked on `BRIEFING.md` as Phase 1 of the 6-phase initiative (Phase 2: two-way space×time + sparse fast path; Phase 3: ring-indicator spillover-aware DiD per Butts 2021; Phase 4a/4b: mechanical extension to IF-aggregation and sandwich-derived estimators; Phase 5: survey design support).
+- **Conley (1999) spatial-HAC standard errors via `vcov_type="conley"`** on cross-sectional `LinearRegression` / `compute_robust_vcov` (Phase 1 of the spillover-conley initiative). Keyword arguments: `conley_coords` (n × 2 array of lat/lon or projected coords), `conley_cutoff_km=<float>` (positive finite bandwidth in km for haversine, or coord units for euclidean — REQUIRED, no default per the no-silent-failures contract), `conley_metric="haversine"|"euclidean"|callable` (default `"haversine"`; great-circle uses Earth's mean radius 6371.01 km matching R `conleyreg`), `conley_kernel="bartlett"|"uniform"` (default `"bartlett"` is PSD-guaranteed; `"uniform"` emits `UserWarning` if the meat has a materially negative eigenvalue per Conley 1999 footnote 11). Variance estimator `Var̂(β) = (X'X)^{-1} · ( Σ_{i,j} K(d_ij/h) · X_i ε_i ε_j X_j' ) · (X'X)^{-1}` (Conley 1999 Eq 4.2). **Panel estimators (`DifferenceInDifferences`, `TwoWayFixedEffects`, `MultiPeriodDiD`) reject `vcov_type="conley"` at fit-time with `NotImplementedError`** — Phase 1's cross-sectional Conley does not handle the time dimension. Applying it over (unit, time) rows would treat same-unit cross-time pairs as `d_ij = 0 → K = 1`, mishandling the space-time HAC. Practitioners needing Conley with a panel design should pre-collapse to per-unit first-differences and call `compute_robust_vcov` directly on a single-period regression. Phase 2 will add the space-time product kernel (Driscoll-Kraay) for full panel support. `SyntheticDiD(vcov_type="conley")` raises `TypeError` (uses bootstrap variance, not analytical sandwich); `set_params` mirrors the constructor rejection. `vcov_type="conley"` + `cluster_ids=` / `weights=` / `survey_design=` raises `NotImplementedError` (combined product kernel + Bertanha-Imbens 2014 weighted-Conley deferred to follow-up phases). `n > 20_000` emits a `UserWarning` about the dense O(n²) distance-matrix memory; sparse k-d-tree fast path is queued for Phase 2. Helpers live in new module `diff_diff/conley.py` (`_haversine_km`, `_pairwise_distance_matrix`, `_bartlett_kernel`, `_uniform_kernel`, `_validate_conley_kwargs`, `_compute_conley_vcov`); `compute_robust_vcov` in `diff_diff/linalg.py` imports the dispatch helpers. R `conleyreg` parity (Düsterhöft 2021, CRAN v0.1.9) on three benchmark fixtures (`benchmarks/data/r_conleyreg_conley_golden.json`, regenerable via `benchmarks/R/generate_conley_golden.R`); observed max abs diff 5.7e-16. Earth radius 6371.01 km matches `conleyreg::haversine_dist`. Test file `tests/test_conley_vcov.py` skips parity cleanly when the JSON is absent. New REGISTRY section `## ConleySpatialHAC`. Tracked on `BRIEFING.md` as Phase 1 of the 6-phase initiative (Phase 2: space-time product kernel + sparse fast path + panel-estimator support; Phase 3: ring-indicator spillover-aware DiD per Butts 2021; Phase 4a/4b: mechanical extension to IF-aggregation and sandwich-derived estimators; Phase 5: survey design support).
 - **Tutorial 21: HAD Pre-test Workflow** (`docs/tutorials/21_had_pretest_workflow.ipynb`) — composite pre-test walkthrough for `HeterogeneousAdoptionDiD` building on Tutorial 20's brand-campaign framing. Uses a 60-DMA × 8-week panel close in shape to T20's but with the dose distribution drawn from `Uniform[$0.01K, $50K]` (vs T20's `[$5K, $50K]`); the true support is strictly positive but very near zero, chosen so the QUG step in `did_had_pretest_workflow` fails-to-reject `H0: d_lower = 0` in this finite sample and the verdict text fires the load-bearing "Assumption 7 deferred" pivot for the upgrade-arc narrative. (HAD's `design="auto"` selector — a separate min/median heuristic at `had.py::_detect_design`, NOT the QUG p-value — independently lands on the `continuous_at_zero` identification path with target `WAS` on this panel because `d.min() < 0.01 * median(|d|)`. The QUG test and the design selector are independent rules that point to the same identification path here.) Walks through three surfaces: (a) `did_had_pretest_workflow(aggregate="overall")` on a two-period collapse, where the verdict explicitly flags Step 2 (Assumption 7 pre-trends) as not run because a single pre-period structurally cannot support a pre-trends test, and the structural fields `pretrends_joint` / `homogeneity_joint` are both `None`; (b) `did_had_pretest_workflow(aggregate="event_study")` on the full multi-period panel, where the verdict reads "TWFE admissible under Section 4 assumptions" because all three testable diagnostics (QUG + joint pre-trends Stute over 3 horizons + joint homogeneity Stute over 4 horizons) fail-to-reject — non-rejection evidence under finite-sample power and test specification, not proof that the identifying assumptions hold; and (c) a side panel exercising both `yatchew_hr_test` null modes — `null="linearity"` (default, paper Theorem 7) vs `null="mean_independence"` (Phase 4 R-parity with R `YatchewTest::yatchew_test(order=0)`) — on the within-pre-period first-difference paired with post-period dose, illustrating the stricter null's larger residual variance (`sigma2_lin` 7.01 vs 6.53) and smaller p-value (0.29 vs 0.49). Companion drift-test file `tests/test_t21_had_pretest_workflow_drift.py` (16 tests pinning panel composition, both verdict pivots, structural anchors on both paths, deterministic QUG / Yatchew statistics, bootstrap p-value tolerance bands per `feedback_bootstrap_drift_tests_need_backend_tolerance`, and `HAD(design="auto")` resolution to `continuous_at_zero` on this panel). T20's "Composite pretest workflow" Extensions bullet updated with a forward-pointer to T21. T22 weighted/survey HAD tutorial remains queued as a separate notebook PR.
 - **`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).

README.md

@@ -124,7 +124,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [Honest DiD](https://diff-diff.readthedocs.io/en/stable/api/honest_did.html) - Rambachan & Roth (2023) sensitivity analysis: robust CI under PT violations, breakdown values
 - [Pre-Trends Power Analysis](https://diff-diff.readthedocs.io/en/stable/api/pretrends.html) - Roth (2022) minimum detectable violation and power curves
 - [Power Analysis](https://diff-diff.readthedocs.io/en/stable/api/power.html) - analytical and simulation-based MDE, sample size, power curves for study design
-- Conley spatial HAC SE (`vcov_type="conley"`) on DifferenceInDifferences/TwoWayFixedEffects/MultiPeriodDiD - Conley (1999) spatial-correlation-aware SEs with parity vs R `conleyreg`
+- Conley spatial HAC SE (`vcov_type="conley"`) on cross-sectional `LinearRegression` / `compute_robust_vcov` - Conley (1999) spatial-correlation-aware SEs with parity vs R `conleyreg`
 
 ## Survey Support