test(callaway): fold ipw R yardsticks into regenerated golden; record no-cov ipw document-only decision

CHANGELOG.md

@@ -35,6 +35,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     `did_had_pretest_workflow`, ...) are unchanged in this release and removed separately.
 
 ### Testing
+- **`CallawaySantAnna` ipw R-parity yardsticks folded into the golden fixture + no-covariate
+  ipw structural-parity decision recorded.** `csdid_golden_values.json` regenerated (R 4.5.2,
+  did 2.5.1, DRDID 1.3.0): all pre-existing data and result blocks reproduced byte-identically;
+  the ipw scenario now carries the `aggte` simple/dynamic/group blocks (identical to the
+  previously hardcoded 2026-07-05 yardsticks), and
+  `test_golden_ipw_aggregation_se_vs_r_did_251` reads them from the JSON. The no-covariate ipw
+  branch's unconditional-propensity treatment is now a recorded document-only decision
+  (REGISTRY § CallawaySantAnna): R `did`'s intercept-only logit is deliberately not mirrored —
+  its estimation-effect correction is identically zero, and no-covariate ipw/reg/dr reduce to
+  the same difference-in-means IF, locked bit-identical per cell by a new
+  `TestDRNoCovariateSEUniformity::test_ipw_no_cov_per_cell_identical_to_reg`.
 - **CI-locked standard-error parity for flagship and previously-unasserted paths (SE-audit
   coverage batch).** These surfaces computed SEs matching R but had no CI assertion pinning them
   (the latent-risk pattern that once hid the CallawaySantAnna reg-method gap):

TODO.md

@@ -31,8 +31,6 @@ The `Origin` column (Actionable tables) and the `PR` column (Deferred tables) bo
 | `SyntheticControl` conformal (CWZ 2021) extensions: (a) one-sided / signed-`t` variants (§7); (b) covariates in the conformal proxy (`X_jt`, eqs 4/6 — current proxy is outcomes-only); (c) AR / innovation-permutation path (Lemmas 5-7) for time-series proxies. The joint test, pointwise CIs, and average-effect CI have landed. | `conformal.py`, `synthetic_control_results.py` | CWZ-2021 | Heavy | Low |
 | `ContinuousDiD` CGBS-2024 extensions. (a) `covariates=` kwarg — **DONE (reg/dr)**; (b) discrete-treatment saturated regression (`treatment_type="discrete"`) — **DONE**; (c) lowest-dose-as-control per Remark 3.1 when `P(D=0)=0` (`control_group="lowest_dose"`) — **DONE** (discrete + continuous mass-point, single-cohort; estimand `ATT(d)−ATT(d_L)`; see REGISTRY Note #7). Remaining (all deferred `NotImplementedError`, documented): `estimation_method="ipw"` on the dose curve (scalar-adjustment / degenerate); `covariates=` × `survey_design=` (weighted OR + weighted nuisance IF); multi-cohort **heterogeneous-support** discrete aggregation (support-aware: average each dose only over the cohorts that observe it); **multi-cohort `lowest_dose`** (within-cohort `d_L` reference + support-aware cross-cohort aggregation); and **`covariates=` × `lowest_dose`** (conditional-PT-relative-to-`d_L` estimand). Single-cohort / 2-period / shared-support multi-cohort are supported. | `continuous_did.py` | CGBS-2024 | Heavy | Low |
 | `TwoWayFixedEffects(vcov_type in {hc2, hc2_bm})` with replicate-weight designs raises `NotImplementedError` (`twfe.py:~233`). The replicate path re-demeans per replicate, which doesn't compose with the full-dummy HC2/HC2-BM build — a correct impl needs per-replicate full-dummy refit. Workaround: `hc1` for replicate-weight CR1. | `twfe.py::fit` | follow-up | Heavy | Low |
-| `CallawaySantAnna` no-covariate ipw treats the propensity as unconditional (no correction term); R's `did` fits an intercept-only logit whose estimation effect is identically zero in the IF, so values match — decide whether to document-only (current REGISTRY note) or mirror R's code path for structural parity. | `staggered.py::_ipw_estimation` (no-cov branch) | CS-scaling | Quick | Low |
-| Fold the R `did` 2.5.1 ipw aggregation yardsticks (hardcoded with provenance in `test_golden_ipw_aggregation_se_vs_r_did_251`) into `csdid_golden_values.json` on the next fixture regeneration — the generator already emits the ipw `aggte` blocks; switch the test to read the JSON. | `benchmarks/R/generate_csdid_test_values.R`, `tests/test_csdid_ported.py` | CS-scaling | Quick | Low |
 | TWFE's HC2/HC2-BM inline full-dummy build (`twfe.py:280-315`) duplicates the dummy-construction logic in `DifferenceInDifferences(fixed_effects=...)` (`estimators.py:478-486`). Extract a shared helper, or delegate TWFE's HC2/HC2-BM path to DiD's `fixed_effects=` branch (with TWFE-specific cluster-default threading), to reduce drift risk on FE naming / survey behavior / result-surface conventions. Substantive refactor — touches both estimators. | `twfe.py::fit`, `estimators.py::DifferenceInDifferences.fit` | follow-up | Heavy | Low |
 | `HonestDiD` Δ^SD optimal-FLCI affine-estimator optimizer (Nelder-Mead over slope weights) diverges from R `.FLCI.computeFLCI` at intermediate small M (~`[0.005, 0.02]`): the CI **center** shifts up to ~9% on wide pre/post windows while the **width** matches to ~1e-3. Not a local-minimum artifact (multi-start does not move it); the two implementations land on different affine estimators of near-equal length. Coverage is unaffected. Reconcile our FLCI optimization with R's exact algorithm. Exposed when the identified-set NaN gate was removed (finite CIs now surface at all M). | `honest_did.py::_compute_optimal_flci` | SE-audit | Mid | Low |
 

benchmarks/R/generate_csdid_test_values.R

@@ -108,10 +108,12 @@ data2_ipw <- build_sim_dataset(sp2_ipw)
 res2_ipw <- att_gt(yname = "Y", xformla = ~X, data = data2_ipw, tname = "period",
                    idname = "id", gname = "G", est_method = "ipw",
                    bstrap = FALSE, cband = FALSE)
-# Aggregations for the ipw scenario. Until the fixture is regenerated, these
-# values are hardcoded (from did 2.5.1 / DRDID 1.3.0, 2026-07-05) in
-# tests/test_csdid_ported.py::test_golden_ipw_aggregation_se_vs_r_did_251;
-# on the next regeneration the test can switch to reading them from the JSON.
+# Aggregations for the ipw scenario, read by
+# tests/test_csdid_ported.py::test_golden_ipw_aggregation_se_vs_r_did_251.
+# Folded into the JSON on the 2026-07-07 regeneration (did 2.5.1 / DRDID
+# 1.3.0, R 4.5.2); all pre-existing data and result blocks reproduced
+# byte-identically, and the folded values match the previously hardcoded
+# 2026-07-05 yardsticks exactly.
 agg2_ipw_simple <- aggte(res2_ipw, type = "simple", bstrap = FALSE, cband = FALSE)
 agg2_ipw_dynamic <- aggte(res2_ipw, type = "dynamic", bstrap = FALSE, cband = FALSE)
 agg2_ipw_group <- aggte(res2_ipw, type = "group", bstrap = FALSE, cband = FALSE)

benchmarks/data/csdid_golden_values.json

@@ -97,6 +97,24 @@
           "time": [2, 3, 4, 2, 3, 4, 2, 3, 4],
           "att": [0.93713345895, 1.1102267341, 0.68521672467, -0.67162819411, 0.86720176282, 0.35233061573, -0.58336476425, -0.10949916241, 0.24708100638],
           "se": [0.28869526544, 0.45992762299, 0.6484493646, 0.2842568482, 0.26539204819, 0.44734379608, 0.27944370832, 0.28444193554, 0.30682305369]
+        },
+        "simple": {
+          "att": 0.68857256467,
+          "se": 0.31382230012
+        },
+        "group": {
+          "overall_att": 0.58154811452,
+          "overall_se": 0.28417155702,
+          "att": [0.91085897258, 0.60976618928, 0.24708100638],
+          "se": [0.44399702904, 0.33782835505, 0.30682305369],
+          "egt": [2, 3, 4]
+        },
+        "dynamic": {
+          "overall_att": 0.68885444012,
+          "overall_se": 0.38935416434,
+          "att": [-0.58336476425, -0.40369753414, 0.6831798871, 0.69816670859, 0.68521672467],
+          "se": [0.27944370832, 0.22022753056, 0.2015332469, 0.38903747086, 0.6484493646],
+          "egt": [-2, -1, 0, 1, 2]
         }
       }
     },

docs/methodology/REGISTRY.md

@@ -541,6 +541,12 @@ Aggregations:
   `pscore_trim` (R drops at `trim.level=0.995`; differs only at extreme propensities);
   no-covariate ipw is treated as unconditional (R fits an intercept-only logit whose
   estimation effect is identically zero in the IF, so this is presentation-only).
+  **Note:** decided document-only (2026-07-07) — the intercept-only logit is deliberately
+  NOT mirrored structurally: its estimation-effect correction is identically zero at the
+  MLE, so mirroring would add a per-cell IRLS solve (and its non-convergence failure
+  surface) for zero numerical change. No-covariate ipw/reg/dr all reduce to the same
+  difference-in-means IF, bit-identical per cell (locked by
+  `tests/test_methodology_callaway.py::TestDRNoCovariateSEUniformity::test_ipw_no_cov_per_cell_identical_to_reg`).
   DR's no-covariate per-cell SE now also uses the IF-based `sqrt(sum(phi^2))` form (it
   had lagged on the ddof=1 plug-in `sqrt(var_t/n_t + var_c/n_c)`, O(1/n) from R): without
   covariates DR reduces to difference in means, so its per-cell SE is now bit-identical to

tests/test_csdid_ported.py

@@ -1484,19 +1484,22 @@ def test_golden_reg_aggregation_se(self, golden_values):
     def test_golden_ipw_aggregation_se_vs_r_did_251(self, golden_values):
         """Aggregated ipw SEs match R did 2.5.1 ``aggte`` on the golden data.
 
-        The golden JSON carries no aggregation blocks for the ipw scenario,
-        so the expected values are hardcoded from a fresh R run (did 2.5.1,
-        DRDID 1.3.0, ``bstrap=FALSE``, computed 2026-07-05 on the EXACT
-        ``with_covariates_ipw`` fixture data; see
-        benchmarks/R/generate_csdid_test_values.R which will fold these into
-        the JSON on its next regeneration). Discriminating: the pre-fix
-        Python simple SE was 0.32125722 (missing PS estimation-effect
-        correction, ~2.4% off). Observed post-fix agreement ~1e-10; 1e-6
-        covers the fixtures' 8-decimal quantization + IRLS-vs-glm headroom.
+        The expected values are read from the golden JSON's ipw aggregation
+        blocks (``simple`` / ``dynamic`` / ``group``), folded in on the
+        2026-07-07 regeneration (did 2.5.1 / DRDID 1.3.0, ``bstrap=FALSE``;
+        identical to the previously hardcoded 2026-07-05 yardsticks — see
+        benchmarks/R/generate_csdid_test_values.R). Discriminating: the
+        pre-fix Python simple SE was 0.32125722 (missing PS
+        estimation-effect correction, ~2.4% off). Observed post-fix
+        agreement ~1e-10; 1e-6 covers the fixtures' quantization +
+        IRLS-vs-glm headroom.
         """
         if "with_covariates_ipw" not in golden_values:
             pytest.skip("Scenario not in golden values")
         scenario = golden_values["with_covariates_ipw"]
+        r_results = scenario["results"]
+        if "simple" not in r_results:
+            pytest.skip("ipw aggregation blocks not in golden values (pre-2026-07 fixture)")
         data = _golden_to_df(scenario["data"])
         cs = CallawaySantAnna(estimation_method="ipw")
         results = cs.fit(
@@ -1509,28 +1512,20 @@ def test_golden_ipw_aggregation_se_vs_r_did_251(self, golden_values):
             aggregate="all",
         )
         # R did 2.5.1: aggte(type="simple", bstrap=FALSE, cband=FALSE)
-        assert abs(results.overall_att - 0.68857256) < 1e-6
-        assert abs(results.overall_se - 0.31382230) < 1e-6
+        assert abs(results.overall_att - r_results["simple"]["att"]) < 1e-6
+        assert abs(results.overall_se - r_results["simple"]["se"]) < 1e-6
         # aggte(type="dynamic"): (att.egt, se.egt) per event time
-        r_dynamic = {
-            -2: (-0.58336476, 0.27944371),
-            -1: (-0.40369753, 0.22022753),
-            0: (0.68317989, 0.20153325),
-            1: (0.69816671, 0.38903747),
-            2: (0.68521672, 0.64844936),
-        }
-        for e, (r_att, r_se) in r_dynamic.items():
-            es = results.event_study_effects[e]
+        r_dyn = r_results["dynamic"]
+        assert len(r_dyn["egt"]) > 0
+        for e, r_att, r_se in zip(r_dyn["egt"], r_dyn["att"], r_dyn["se"]):
+            es = results.event_study_effects[int(e)]
             assert abs(es["effect"] - r_att) < 1e-6, f"ipw dyn ATT(e={e})"
             assert abs(es["se"] - r_se) < 1e-6, f"ipw dyn SE(e={e})"
         # aggte(type="group"): (att.egt, se.egt) per cohort
-        r_group = {
-            2: (0.91085897, 0.44399703),
-            3: (0.60976619, 0.33782836),
-            4: (0.24708101, 0.30682305),
-        }
-        for g, (r_att, r_se) in r_group.items():
-            grp = results.group_effects[g]
+        r_grp = r_results["group"]
+        assert len(r_grp["egt"]) > 0
+        for g, r_att, r_se in zip(r_grp["egt"], r_grp["att"], r_grp["se"]):
+            grp = results.group_effects[int(g)]
             assert abs(grp["effect"] - r_att) < 1e-6, f"ipw group ATT(g={g})"
             assert abs(grp["se"] - r_se) < 1e-6, f"ipw group SE(g={g})"
 

tests/test_methodology_callaway.py

@@ -422,6 +422,38 @@ def test_dr_no_cov_aggregated_se_matches_reg(self):
             else:
                 np.testing.assert_allclose(dr_es["se"], reg_es["se"], rtol=0, atol=1e-12)
 
+    def test_ipw_no_cov_per_cell_identical_to_reg(self):
+        """No-covariate ipw is bit-identical to reg per-cell (effect AND SE).
+
+        Locks the documented decision (REGISTRY § CallawaySantAnna) that the
+        no-covariate ipw branch treats the propensity as unconditional and
+        does NOT structurally mirror R ``did``'s intercept-only logit: the
+        logit's estimation-effect correction is identically zero at the MLE,
+        so both implementations reduce to the same difference-in-means IF —
+        mirroring would add a per-cell IRLS solve (and its failure surface)
+        for zero numerical change. If this test ever breaks, the ipw no-cov
+        branch has diverged from the diff-in-means contract and the REGISTRY
+        note must be revisited."""
+        data = generate_staggered_data(n_units=120, n_periods=6, never_treated_frac=0.3, seed=7)
+        reg = self._fit(data, "reg", aggregate="event_study")
+        ipw = self._fit(data, "ipw", aggregate="event_study")
+
+        assert set(reg.group_time_effects) == set(ipw.group_time_effects)
+        compared = 0
+        for key, reg_cell in reg.group_time_effects.items():
+            ipw_cell = ipw.group_time_effects[key]
+            if not np.isfinite(reg_cell["se"]):
+                assert not np.isfinite(ipw_cell["se"])
+                continue
+            np.testing.assert_allclose(ipw_cell["effect"], reg_cell["effect"], rtol=0, atol=1e-12)
+            np.testing.assert_allclose(ipw_cell["se"], reg_cell["se"], rtol=0, atol=1e-12)
+            compared += 1
+        assert compared >= 3, f"expected several finite cells to compare, got {compared}"
+
+        # Aggregated surfaces inherit the identity (same IF feeds aggregation).
+        np.testing.assert_allclose(ipw.overall_att, reg.overall_att, rtol=0, atol=1e-12)
+        np.testing.assert_allclose(ipw.overall_se, reg.overall_se, rtol=0, atol=1e-12)
+
 
 # =============================================================================
 # Phase 2: R Benchmark Comparison Tests