+- **`SpilloverDiD` — ring-indicator spillover-aware DiD (Butts 2021).** New standalone estimator at `diff_diff/spillover.py` implementing two-stage Gardner methodology with ring-indicator covariates that identify direct effect on treated (`tau_total`) alongside per-ring spillover effects on near-control units (`delta_j`). Documented synthesis of ingredients (no single published software covers the exact recipe — `did2s` implements Gardner two-stage without rings; the Butts ring estimator has no R/Stata package): Butts (2021) Section 5 / Table 2 identification, Gardner (2022) two-stage residualize-then-fit, and the Conley spatial-HAC vcov shipped in 3.3.3. Handles both panel non-staggered (Equations 5/6/8) and Section 5 staggered timing in one estimator — non-staggered is the special case where all treated units share an onset time. **API:** `SpilloverDiD(rings=[0, 50, 100, 200], conley_coords=("lat","lon"), ...).fit(data, outcome="y", unit="unit", time="t", treatment="D")` (binary D auto-converted to `first_treat`) or `.fit(..., first_treat="first_treat")` (Gardner convention). Result: `SpilloverDiDResults(DiDResults)` with `.att` = `tau_total`, `.spillover_effects` (per-ring `pd.DataFrame` with `coef`/`se`/`t_stat`/`p_value`/`ci_low`/`ci_high`), `.ring_breakpoints`, `.d_bar`, `.n_units_ever_in_ring`, `.n_far_away_obs`, `.is_staggered`. `.coefficients` exposes all `(1+K)` stage-2 entries (`"treatment"` + `"_spillover_<ring_label>"`) plus an `"ATT"` alias keyed to vcov columns. **Methodology spec (committed):** stage-2 regressor is the time-varying `(1 - D_it) * Ring_{it,j}` form (paper page 12's `S_it = S_i * 1{t >= t_treat}` notation; Section 5 Table 2's `S^k_{it}` / `Ring^k_{it,j}`). Reading the literal unit-static `(1 - D_it) * S_i` from Equation 5 is algebraically rank-deficient under TWFE (`(1-D_it) * S_i = S_i - D_it`, with `S_i` absorbed by `mu_i`, leaving `-D_it`); only the time-varying form supports the paper's identification (Proposition 2.3). Stage-1 subsample uses Butts' STRICTER `Omega_0 = {D_it = 0 AND S_it = 0}` (untreated AND unexposed), not TwoStageDiD's `{D_it = 0}` alone — this prevents spillover-contaminated near-controls in pre/post periods from biasing the time FE. **Gardner identity (non-staggered):** a 20-seed deterministic regression test pins `SpilloverDiD.att` against a direct single-stage TWFE ring regression on the full sample (`y ~ mu_i + lambda_t + tau * D_it + sum_j delta_j * (1 - D_it) * Ring_{it,j}`) at `atol=1e-10` — empirically bit-identical, so the reported non-staggered `tau_total` IS the Butts Eqs. 4-6 estimator. **Identification-check policy (period strict, unit warn-and-drop, plus connectivity):** every period must have at least one Omega_0 row (hard `ValueError` — dropping a period removes all units' cross-time identification). Units lacking Omega_0 rows (e.g. baseline-treated units with `D_it = 1` at every observed `t`) are warned-and-dropped: their unit FE is NaN, residualization writes NaN on their rows, and the downstream finite-mask path excludes them from stage 2 — mirrors `TwoStageDiD`'s always-treated convention. Additionally, the supported-units bipartite graph (units linked by shared Omega_0 periods) must form a single connected component; `K > 1` components raise `ValueError` because the FE solver would return only component-specific constants and residualization would silently mix them across components (defense-in-depth — under absorbing treatment the disconnected case may be unreachable through the upstream validators, but the check future-proofs Wave B follow-ups). **Public API restrictions (Wave B MVP):** `covariates=` raises `NotImplementedError` because Gardner-style two-stage requires covariate effects estimated on the untreated-and-unexposed subsample at stage 1 (appending raw covariates only at stage 2 silently biases `tau_total` / `delta_j` on panels with time-varying covariates); non-absorbing / reversible treatment patterns (e.g. `[0, 1, 0]`) raise `ValueError` rather than being silently coerced into "treated from first 1 onward"; non-constant `first_treat` values across rows of the same unit raise `ValueError`; `conley_coords` is required on every fit path (not just `vcov_type="conley"`) because ring construction always uses it. **Far-away control identification:** uses CURRENT-period untreated status (`D_it = 0`) rather than never-treated-only, so all-eventually-treated staggered designs (no never-treated units) can identify the counterfactual via not-yet-treated far-away rows. **Variance (Wave B MVP):** stage-2 OLS variance via `solve_ols` (HC1 / Conley / cluster paths all flow through). The Gardner GMM first-stage uncertainty correction is NOT applied at stage 2 in this PR (documented limitation; planned follow-up extends `two_stage.py::_compute_gmm_variance` to accept a Conley kernel matrix in place of HC1's identity at the influence-function outer-product step). **Deferred features (planned follow-ups):** `event_study=True` per-event-time × ring coefficients (Butts Table 2), `survey_design=` integration, `ring_method="count"` (count-of-treated-in-ring), data-driven `d_bar` selection (Butts 2021b / Butts 2023 JUE Insight), Gardner GMM first-stage correction at stage 2, sparse staggered ring-distance path. **Tests:** `tests/test_spillover.py` (157 tests across ring-construction primitives, validators, fit integration, raw-data invariant, identification MC — non-staggered DGP at 50 seeds + 200-seed `@pytest.mark.slow` variant recovers both `tau_total` and `delta_1`; staggered DGP at 30 seeds anchors both `tau_total` and `delta_1` — Conley plumbing (verifies `solve_ols` is called with `vcov_type="conley"` + Conley kwargs, no silent HC1 fallback), Gardner identity bit-identity, coefficients-vs-vcov alignment, warn-and-drop, rank_deficient_action validation, Omega_0 bipartite-graph connectivity, anticipation behavior on both fit paths). DGP factories `tests/_dgp_utils.py::generate_butts_nonstaggered_dgp` / `generate_butts_staggered_dgp` satisfy Butts Assumptions 1/3/5/7 by construction.
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