feat(v4): item 7c of the 4.0 cut - SpilloverDiD df_convention knob (M-154)

TODO.md

@@ -86,7 +86,7 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m
 
 | Issue | Location | Origin | Effort | Priority |
 |-------|----------|--------|--------|----------|
-| Re-execute the nine tutorials whose committed outputs still show the 3.x residual-df p-values/CIs after the `df_convention` default moved to `"cluster"` (t(G-1)): `01_basic_did`, `02_staggered_did`, `04_parallel_trends`, `05_honest_did`, `07_pretrends_power`, `09_real_world_examples`, `11_imputation_did`, `13_stacked_did`, `16_wooldridge_etwfe` (not `16_survey_did`: the survey df wins). Run under `DIFF_DIFF_BACKEND=rust` with the per-worktree kernel, reconcile any prose that restates a moved number, and `--nbmake` each; scheduled for the 4.0 release PR (`docs/v4-design.md` section 9 item 10) together with any item-7c/8/9 movers (24 was refreshed in #879; 23 in 7b and again in 7c). 7b's label changes also drift any committed analytical DiD/TWFE summary (", G=" on the variance line) and every SunAbraham summary (the new "Std. errors:" line) - covered by the same re-execution. | `docs/tutorials/*.ipynb` | #879 | Mid | Medium |
+| Re-execute the nine tutorials whose committed outputs still show the 3.x residual-df p-values/CIs after the `df_convention` default moved to `"cluster"` (t(G-1)): `01_basic_did`, `02_staggered_did`, `04_parallel_trends`, `05_honest_did`, `07_pretrends_power`, `09_real_world_examples`, `11_imputation_did`, `13_stacked_did`, `16_wooldridge_etwfe` (not `16_survey_did`: the survey df wins). Run under `DIFF_DIFF_BACKEND=rust` with the per-worktree kernel, reconcile any prose that restates a moved number, and `--nbmake` each; scheduled for the 4.0 release PR (`docs/v4-design.md` section 9 item 10) together with any item-8/9 movers (24 was refreshed in #879; 23 in 7b, re-validated in 7c - it prints no p-values or CIs). 7b's label changes also drift any committed analytical DiD/TWFE summary (", G=" on the variance line) and every SunAbraham summary (the new "Std. errors:" line) - covered by the same re-execution. | `docs/tutorials/*.ipynb` | #879 | Mid | Medium |
 | TWFE event-study one-way HC2-BM absorbed-FE R parity against the `mpd_absorbed_fe_did` golden (`clubSandwich::vcovCR(cluster=1:n, type="CR2")`): since 4.0 (item 7b) `cluster=False` + hc2_bm reaches the one-way lane directly (no singleton-cluster column needed; `tests/test_v4_cluster_policy.py` pins finiteness and the static-surface LinearRegression parity only), so the open item is the R pin itself; the hc2 lane is pinned by `TestTWFEEventStudyAbsorbedFERParity` and the clustered hc2_bm per-period lane by `test_twfe_event_study_cluster_hc2_bm_per_period_df_matches_clubsandwich`; the MultiPeriodDiD lanes died at the 4.0 cut (PR 4c-ii) | `tests/test_estimators_vcov_type.py` | #v4-4c-i | Mid | Medium |
 | TWFE event-study power profile: `simulate_power`/`simulate_mde`/`simulate_sample_size` have no `_EstimatorProfile` for `TwoWayFixedEffects.fit(event_study=True)` since the MultiPeriodDiD registry entry went with the class (PR 4c-ii); `EventStudyResults` carries no scalar headline, so the profile needs a design (post-period average via the `tests/helpers/event_study_average.py` arithmetic, or a named period) plus its `_SURVEY_FIT_BUILDERS` twin | `diff_diff/power.py` | #v4-4c-ii | Mid | Low |
 | Clustered hc2_bm Bell-McCaffrey DOF guard is batch-relative (`linalg._cr2_bm_dof_inner`: a contrast is NaN'd when its scaled max\|B\| falls below 1e-10 of the batch maximum), so the event-study core keeps the otherwise-unused post-period-average contrast column as the anchor to hold the 3.x per-period NaN masks bit-identical (PR 4c-ii, user decision 2026-10-25). Reproduction: `x_nc = treated * 1[period >= 4] + 3e-6 * default_rng(seed).normal(size=n)` as a covariate on the oracle panel flips the mask between `[eye(k), avg]` and `eye(k)` on 20/20 seeds (cond(X) ~ 9e5; 1e-7 never flips, <= 3e-8 rank-drops) - too BLAS-sensitive for a committed oracle, hence the structural pin `test_hc2_bm_contrast_batch_keeps_post_average_anchor`. Decide whether the guard should use a batch-independent anchor (the treatment contrast, or the absolute floor only) and then drop the column | `diff_diff/estimators.py`, `diff_diff/linalg.py` | #v4-4c-ii | Mid | Medium |
@@ -99,7 +99,7 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m
 | Evaluate adding the `BaseEstimator` param surface (get_params/set_params) to the exported classes that never had it - `PowerAnalysis`, `LinearRegression`, `BusinessReport`, `DiagnosticReport`, `TWFEWeightsResult` (a NEW public surface, deliberately out of the 2(c)-i pure-refactor scope; `LinearRegression` is the one `fit`-bearing class excluded from the contract suite's roster-completeness test). | `diff_diff/linalg.py`, `diff_diff/power.py` | mixin PR | Mid | Low |
 | Tighten the mypy suppressions that back the enforced-zero posture: burn down `prep_dgp`'s per-module `[index]` override (needs a None-vs-array restructure that preserves the seeded RNG stream), and evaluate re-enabling the globally disabled codes (`arg-type`, `return-value`, `var-annotated`, `assignment`) one at a time — `assignment` alone hid several real annotation drifts found during the 2026-07 triage. | `pyproject.toml` `[tool.mypy]`, `diff_diff/prep_dgp.py` | lint-CI | Mid | Low |
 | Route LWDiD into the practitioner code surfaces: `practitioner_next_steps` (`diff_diff/practitioner.py`), `agent_workflow.py`, `DiagnosticReport`, and `BusinessReport` never dispatch to or mention LWDiD — the AI guides now cover it, but the code surfaces need contract decisions (when to recommend it, what diagnostics to run) plus tests | `diff_diff/practitioner.py`, `diff_diff/agent_workflow.py`, `diff_diff/diagnostic_report.py`, `diff_diff/business_report.py` | LWDiD release audit | Mid | Low |
-| df-provenance completion follow-up (PR C descoping): a Wooldridge public per-row df channel (a per-key dict that can also carry the hc2_bm BM contrast dofs the scalar `_df_analytic_fallback` cannot represent — the unified surface's Wooldridge df column stays NaN until then); ImputationDiD NON-survey per-lead df (the survey-fit scalar `event_study_df` + its bootstrap clearing shipped with the M-092 completion; what remains is surfacing the `resolve_tail_df`-derived per-lead dfs on non-survey fits, which needs the `_compute_lead_coefficients` return-path change); and the `inference_df` (DiD/SA/StackedDiD) vs `df_inference` (CS/dCDH) adapter-naming unification. NOTE (M-024): StackedDiD's `df_survey=None` on analytical fits is DELIBERATE - the container resolver's `df_inference` fallback not matching `inference_df` is load-bearing (normal-theory FLCI; see the REGISTRY StackedDiD M-024 Note and the pin in `tests/test_event_study_consumers.py`) - the unification must preserve that outcome or renegotiate it explicitly. | `diff_diff/wooldridge_results.py`, `diff_diff/imputation.py`, `diff_diff/results_base.py` | #variance-PR-C | Mid | Low |
+| df-provenance completion follow-up (PR C descoping): a Wooldridge public per-row df channel (a per-key dict that can also carry the hc2_bm BM contrast dofs the scalar `_df_analytic_fallback` cannot represent — the unified surface's Wooldridge df column stays NaN until then); ImputationDiD NON-survey per-lead df (the survey-fit scalar `event_study_df` + its bootstrap clearing shipped with the M-092 completion; what remains is surfacing the `resolve_tail_df`-derived per-lead dfs on non-survey fits, which needs the `_compute_lead_coefficients` return-path change); and the `inference_df` (DiD/SA/StackedDiD/SpilloverDiD) vs `df_inference` (CS/dCDH) adapter-naming unification. NOTE (M-024): StackedDiD's `df_survey=None` on analytical fits is DELIBERATE - the container resolver's `df_inference` fallback not matching `inference_df` is load-bearing (normal-theory FLCI; see the REGISTRY StackedDiD M-024 Note and the pin in `tests/test_event_study_consumers.py`) - the unification must preserve that outcome or renegotiate it explicitly. | `diff_diff/wooldridge_results.py`, `diff_diff/imputation.py`, `diff_diff/results_base.py` | #variance-PR-C | Mid | Low |
 | `worktree-rm` safety via a tested argv helper: the prose rewrite (ask-before-remove confirmation gate, detached-HEAD reachability/rescue, tip-identity force-delete guard) was reverted to the main version because editing the prose repeatedly reintroduced shell-injection (last: sourcing a state file built from a git-derived branch name). Restore those guards in a `worktree_rm.py` that takes the name via file ingress, invokes git through argv arrays, and has a metacharacter-branch/path injection regression test — the pattern that worked for `pr_prepare.py`/`premerge_scan.py`. | `.claude/commands/worktree-rm.md`, `.claude/scripts/` | skill-audit | Heavy | Medium |
 | `premerge_scan.py` should scan the staged blob (`git show :path`) for staged methodology files, not the working-tree copy — a stage-then-revert-working-copy edit currently reads the safe working version and misses the staged violation. Union staged-index findings with unstaged/untracked filesystem findings. | `.claude/scripts/premerge_scan.py` | skill-audit | Mid | Low |
 | Carousel PDF drift guard: the committed deck PDFs are build artifacts of their `generate_*.py` scripts (README rule: regenerate in the same commit), but nothing verifies a committed PDF matches its generator — a source edit without regeneration, or a stale raster chart, passes every claims test. Applies to ALL decks, not just the MMM one. fpdf embeds creation-date metadata, so a byte-compare needs fixed metadata or a per-page rendered-text manifest. Origin: mmm-carousel local review R1. | `carousel/`, `tests/` | mmm-carousel | Mid | Low |

changelog.d/20261029-v4-pr7c-m154-spillover-df-convention.md

@@ -0,0 +1,7 @@
+### Breaking Changes
+
+- `SpilloverDiD` gains the three-value `df_convention` knob with the 4.0 default `"cluster"` ([M-154], item 7c of the 4.0 cut): p-values and confidence intervals on its clustered lanes - the unit auto-cluster and an explicit `cluster=` - now use the Stata/fixest `t(G - 1)` reference like every other auto-clustered default, with `G` the effective cluster count reported as `n_clusters`. Point estimates, standard errors and t-statistics never move. `df_convention="residual"` restores the 3.x residual-df inference bit-for-bit; `"normal"` is deliberate z. `"cluster"` falls back to the residual df on `cluster=False` and on every `vcov_type="conley"` fit, an explicit product-kernel `cluster=` included (`"normal"` still selects z there); a `survey_design` df keeps precedence under every value; a saturated stage-2 sample stays all-NaN under every value. `SpilloverDiDResults` reports `df_convention` and `inference_df` (also in `to_dict()`).
+
+### Internal
+
+- Ledger: M-154 -> `done`, pinned by `tests/test_v4_spillover_df_convention.py`; the df ladder is the pure helper `spillover._resolve_inference_df` (survey df -> saturation -> `utils.resolve_tail_df`). The variance-conventions matrix gains the `spillover_cluster_unit` / `spillover_cluster_unit_default_df` rows; the REGISTRY `df_convention` knob roster, `docs/methodology/variance-conventions.md`, the migration guide, `llms-full.txt` and `docs/api/spillover.rst` describe the knob; tutorial 23 re-validated (it prints no p-values or CIs).

diff_diff/guides/llms-full.txt

@@ -520,6 +520,7 @@ SpilloverDiD(
     conley_cutoff_km: float | None = None,
     conley_lag_cutoff: int | None = None,
     cluster: str | None | False = None,  # None = auto-cluster at unit (4.0; dropped on Conley, never a survey PSU); False = observation-level HC1 (the 3.x default); str = that column
+    df_convention: str = "cluster",   # Analytical t/p/CI df (4.0, M-154): "cluster" (G-1 on the unit auto-cluster / explicit cluster=; default), "residual" (n-k; the 3.x inference), "normal" (z); "cluster" falls back to n-k on Conley and cluster=False ("normal" still z there); survey df keeps precedence under every value; results carry df_convention / inference_df
     alpha: float = 0.05,
     anticipation: int = 0,  # non-negative integer; bool rejected
     event_study: bool = False,           # Wave C: per-event-time × ring decomposition (Butts Table 2)