feat(v4): PR 3b of the 4.0 cut - remove the fit-time aggregate=/balance_e= keywords on CS, EfficientDiD, StackedDiD, dCDH and HAD

benchmarks/python/benchmark_callaway.py

@@ -124,7 +124,7 @@ def main():
         )
         # Post-fit aggregations stay inside the timed region: the R `aggte`
         # yardstick is fit + simple/dynamic/group aggregation (the 3.x
-        # fit(aggregate="all") route, deprecated since 3.9 and removed at the 4.0 cut).
+        # fit(aggregate="all") route, removed at 4.0).
         es = results.aggregate("event_study")
         grp = results.aggregate("group")
 

benchmarks/python/benchmark_stacked_did.py

@@ -96,8 +96,7 @@ def main():
     )
 
     # The event-study surface is always computed at fit (3.9, row M-024);
-    # the deprecated aggregate= kwarg would emit a FutureWarning inside
-    # the timed block and contaminate the measurement.
+    # fit() takes no aggregate= kwarg since 4.0.
     with Timer() as estimation_timer:
         results = est.fit(
             df,

benchmarks/speed_review/bench_callaway.py

@@ -66,7 +66,7 @@ def bench_fit(n_units, n_bootstrap=0, covariates=None, n_cohorts=5, n_runs=3,
             covariates=covariates,
         )
         # Keep "fit + aggregations" as the measured work (the 3.x
-        # fit(aggregate="all") route, deprecated since 3.9 and removed at the 4.0 cut).
+        # fit(aggregate="all") route, removed at 4.0).
         res.aggregate("event_study")
         res.aggregate("group")
         elapsed = time.perf_counter() - start

benchmarks/speed_review/bench_solve_ols_fastpath.py

@@ -126,7 +126,7 @@ def fit(d):
                 covariates=covs,
             )
             # fit + aggregations is the measured work (the 3.x
-            # fit(aggregate="all") route, deprecated since 3.9 and removed at the 4.0 cut).
+            # fit(aggregate="all") route, removed at 4.0).
             res.aggregate("event_study")
             res.aggregate("group")
             return float(res.overall_att), float(res.overall_se)

benchmarks/speed_review/validate_results.py

@@ -65,7 +65,7 @@ def run_estimator(df, estimation_method="reg", covariates=None, control_group="n
         first_treat="first_treat",
         covariates=covariates,
     )
-    # Post-fit containers (the 3.x fit(aggregate="all") route is deprecated and goes at the 4.0 cut);
+    # Post-fit containers (the 3.x fit(aggregate="all") route, removed at 4.0);
     # the fit is bootstrapped, so each call replays the fit-time bootstrap.
     es_container = results.aggregate("event_study")
     grp_container = results.aggregate("group")

changelog.d/20261021-v4-pr3b-aggregate-removals-mechanical.md

@@ -0,0 +1,55 @@
+### Removed
+- **The fit-time `aggregate=` / `balance_e=` keywords are gone on the estimators whose post-fit
+  route already existed (ledger rows flipped to `removed`).** `fit(aggregate=)` is removed from
+  `CallawaySantAnna` (`M-020`), `EfficientDiD` (`M-023`), `StackedDiD` (`M-024`),
+  `ChaisemartinDHaultfoeuille` (`M-026`) and `HeterogeneousAdoptionDiD` (`M-027`),
+  `fit(balance_e=)` from `CallawaySantAnna` (`M-117`) and `EfficientDiD` (`M-120`), and
+  `aggregate=` from `did_had_pretest_workflow` (`M-139`; the `HADPretestReport.aggregate`
+  output field survives). The successor is post-fit `results.aggregate(type, balance_e=)`; HAD
+  selects its mode from the panel shape. With the keywords go StackedDiD's fit-time
+  `group`/`all`/unknown-value `ValueError`s (`aggregate('group')` already fails closed in the
+  mixin) and the private `_VALID_AGGREGATES` sets in `had.py` / `had_pretests.py`
+  (`_infer_aggregate_mode` is the sole selector). `ImputationDiD`, `TwoStageDiD` and
+  `ContinuousDiD` keep their deprecated keywords until the next cut PR adds the
+  bootstrapped-fit relay.
+- **CallawaySantAnna and EfficientDiD compute no event-study/group surface at fit.** Their
+  legacy `event_study_effects` / `group_effects` fields - and CS's `cband_crit_value`,
+  `event_study_vcov`, `event_study_vcov_index`, `event_study_df` - are `None` after EVERY fit;
+  the post-fit replay (`aggregate('event_study'|'group', balance_e=)`) is the only bootstrap
+  route and is unchanged.
+
+### Breaking Changes
+- Passing `aggregate=` or `balance_e=` to `CallawaySantAnna.fit`, `EfficientDiD.fit`,
+  `StackedDiD.fit`, `ChaisemartinDHaultfoeuille.fit` or `HeterogeneousAdoptionDiD.fit` (or
+  `aggregate=` to `did_had_pretest_workflow`) now raises Python's native
+  `TypeError: ... unexpected keyword argument`. `TripleDifference`'s staggered-mode
+  `aggregate=`/`balance_e=` are untouched (`M-140`/`M-141`, canonical until the DDD container
+  unification).
+- **Positional slots after the removed parameters shift left.** `CallawaySantAnna`:
+  `survey_design` 9 -> 7; `EfficientDiD`: `survey_design` 9 -> 7, `store_eif` 10 -> 8;
+  `StackedDiD` (`aggregate` sat before them): `population`, `survey_design`, `covariates`
+  7/8/9 -> 6/7/8; `ChaisemartinDHaultfoeuille`: `L_max`, `covariates`, `trends_linear`,
+  `trends_nonparam`, `honest_did`, `heterogeneity`, `design2`, `survey_design` each move one
+  slot left (7..14 -> 6..13). `HeterogeneousAdoptionDiD.fit` shifts nothing (everything after
+  the removed slot is keyword-only; a seventh positional now raises). Pass these by keyword.
+- `CallawaySantAnnaResults.to_dataframe(level="event_study"|"group")` and the
+  `EfficientDiDResults` twins raise on every fresh fit (the legacy fields are always `None`);
+  use `results.aggregate(...).to_dataframe()`. The column-schema rewire of these levels onto the
+  container is `M-093`'s (PR 8).
+
+### Changed
+- Runtime messages, the four `llms*.txt` guides, the API docs, the migration guide and the
+  benchmarks no longer steer users to the fit-time route on these five estimators;
+  `CallawaySantAnna`'s class docstring, `plot_event_study`, `honest_did` / `pretrends` and the
+  results-class "not stored" messages point at `results.aggregate('event_study')`.
+
+### Internal
+- `tests/test_aggregate_contract.py`: the `Test*Shim` classes, fit-time fixtures and
+  inertness comparisons for the five estimators are retired in favour of `Test*Removal`
+  pins (TypeError, signature, positional-slot binding); the committed oracle fixture is the
+  numerical reference. `tests/_capture_v4_aggregate_oracles.py` is marked historical (it
+  drives the removed route; runnable only at the recorded `captured_at_head`). Naming-guard
+  allowlist entries for the `llms.txt` / `llms-full.txt` canonical post-fit `aggregate`
+  vocabulary. Ledger row `M-093` records that CS/EfficientDiD populate the legacy fields on
+  no fit. `warn_deprecated_kwarg` loses its last library caller and is retained (spec section
+  8 rule 11) with a direct unit pin.

diff_diff/_deprecation.py

@@ -16,9 +16,14 @@
   default. ``resolve_renamed_kwarg`` maps old to new with a single
   ``FutureWarning``, rejects both-supplied calls loudly, and returns the
   caller-declared default when neither is passed.
-- Dropped parameter (no successor name, e.g. the fit-time ``aggregate=``
-  override): the parameter keeps its position with a sentinel default; the
-  owner calls ``warn_deprecated_kwarg`` when a value was actually supplied.
+- Dropped parameter (no successor name): the parameter keeps its position
+  with a sentinel default; the owner calls ``warn_deprecated_kwarg`` when a
+  value was actually supplied. (The 3.9 fit-time ``aggregate=`` shims on
+  CallawaySantAnna, EfficientDiD, StackedDiD, ChaisemartinDHaultfoeuille and
+  HeterogeneousAdoptionDiD were this shape; they were deleted at the 4.0 cut
+  (PR 3b) and ImputationDiD / TwoStageDiD / ContinuousDiD follow in PR 3c.
+  ``warn_deprecated_kwarg`` has no library caller any more (dCDH was the
+  last) and is retained per section 8 rule 11 for the 5.0 window.)
 - Renamed results field: the dataclass field takes the new name and
   ``deprecated_field_property`` builds the read-only warning alias under the
   old name (a plain class attribute, so it is a descriptor but NOT a

diff_diff/_reporting_helpers.py

@@ -56,11 +56,11 @@ def describe_target_parameter(results: Any) -> Dict[str, Any]:
       disambiguate via the event-study or group aggregate if you need
       the horizon / group target.
     - ``CallawaySantAnna``: ``overall_att`` is cohort-size-weighted
-      across post-treatment ``ATT(g, t)`` cells regardless of the
-      fit-time ``aggregate`` kwarg. The event-study / group tables are
-      produced post-fit via ``results.aggregate('event_study'/'group')``
-      (the deprecated fit-time ``aggregate=`` kwarg populates the legacy
-      ``event_study_effects`` / ``group_effects`` fields until 4.0).
+      across post-treatment ``ATT(g, t)`` cells regardless of any
+      aggregation. The event-study / group tables are produced post-fit
+      via ``results.aggregate('event_study'/'group')``; the legacy
+      ``event_study_effects`` / ``group_effects`` fields are None on
+      every CallawaySantAnna fit since 4.0.
     - ``ContinuousDiD``: the regime (PT vs. SPT) is a user-level
       assumption, not a library setting. The ``definition`` names
       both regime readings (``ATT^loc`` under PT,
@@ -140,9 +140,9 @@ def describe_target_parameter(results: Any) -> Dict[str, Any]:
                 "``overall_att`` is the simple-aggregation headline regardless "
                 "of aggregation choices; event-study and group tables are "
                 "produced post-fit via "
-                "``results.aggregate('event_study'/'group')`` (the deprecated "
-                "fit-time ``aggregate=`` kwarg populates the legacy "
-                "``event_study_effects`` / ``group_effects`` fields until 4.0)."
+                "``results.aggregate('event_study'/'group')`` (the legacy "
+                "``event_study_effects`` / ``group_effects`` fields are None "
+                "on every CallawaySantAnna fit since 4.0)."
             ),
             "aggregation": "simple",
             "headline_attribute": "overall_att",

diff_diff/chaisemartin_dhaultfoeuille.py

@@ -37,7 +37,6 @@
 import pandas as pd
 
 from diff_diff._base import BaseEstimator
-from diff_diff._deprecation import NOT_SUPPLIED, warn_deprecated_kwarg
 from diff_diff.chaisemartin_dhaultfoeuille_bootstrap import (
     ChaisemartinDHaultfoeuilleBootstrapMixin,
 )
@@ -401,8 +400,8 @@ class ChaisemartinDHaultfoeuille(ChaisemartinDHaultfoeuilleBootstrapMixin, BaseE
     - TWFE decomposition diagnostic from Theorem 1 of AER 2020
 
     Aggregation is a post-fit step: ``results.aggregate('event_study')`` /
-    ``results.aggregate('simple')`` (the deprecated fit-time ``aggregate=``
-    never computed anything and now warns; row M-026).
+    ``results.aggregate('simple')``; ``fit()`` takes no aggregation
+    keyword.
 
     Parameters
     ----------
@@ -878,7 +877,6 @@ def fit(
         time: str,
         treatment: str,
         # ---------- forward-compat parameters ----------
-        aggregate: Any = NOT_SUPPLIED,
         L_max: Optional[int] = None,
         covariates: Optional[List[str]] = None,
         trends_linear: Optional[bool] = None,
@@ -914,15 +912,6 @@ def fit(
             within each ``(unit, time)`` cell. Both binary ``{0, 1}`` and
             non-binary (ordinal or continuous) treatment are supported.
             Non-binary treatment requires ``L_max >= 1``.
-        aggregate : str, optional
-            DEPRECATED since 3.9, removed in 4.0 (ledger row M-026).
-            Passing it emits a ``FutureWarning``. The parameter never
-            computed aggregations here (it was reserved and any non-None
-            value has always raised); aggregation is a POST-FIT step:
-            ``results.aggregate('event_study')`` for the multi-horizon
-            surface, ``results.aggregate('simple')`` for the overall
-            estimand. A non-None value now raises ``ValueError`` pointing
-            at that post-fit route.
         L_max : int, optional
             Maximum event-study horizon. When set, computes ``DID_l``
             for ``l = 1, ..., L_max`` using the per-group building block
@@ -1063,28 +1052,8 @@ def fit(
         # ------------------------------------------------------------------
         # Step 2: Forward-compat gates
         # ------------------------------------------------------------------
-        # M-026: fit(aggregate=) is deprecated - the aggregation entry point
-        # is post-fit (results.aggregate()). The param never computed
-        # anything here (any non-None value has always raised), so the shim
-        # warns on supply and rejects non-None values with a pointer at the
-        # post-fit route.
-        if aggregate is not NOT_SUPPLIED:
-            warn_deprecated_kwarg(
-                "ChaisemartinDHaultfoeuille.fit",
-                "aggregate",
-                "aggregation is a post-fit step: "
-                "results.aggregate('event_study') / results.aggregate('simple')",
-            )
-            if aggregate is not None:
-                raise ValueError(
-                    f"fit(aggregate={aggregate!r}) never computed aggregations "
-                    "on ChaisemartinDHaultfoeuille (the parameter was reserved "
-                    "and always raised). Aggregate post-fit instead: "
-                    "results.aggregate('event_study') for the multi-horizon "
-                    "surface (Phase-1 fits without L_max return the 2-row "
-                    "l=1 view), or results.aggregate('simple') for the "
-                    "overall estimand."
-                )
+        # Aggregation is a post-fit step (results.aggregate('event_study') /
+        # results.aggregate('simple')); fit() takes no aggregation keyword.
         _check_forward_compat_gates(
             L_max=L_max,
             controls=controls,
@@ -4395,10 +4364,8 @@ def _check_forward_compat_gates(
     """Raise ``NotImplementedError`` for any non-default Phase 3 parameter.
 
     Phase 2 parameters (``L_max``) are validated inline in ``fit()``
-    after period detection. The deprecated ``aggregate`` parameter is
-    resolved in ``fit()`` itself (row M-026: FutureWarning on supply,
-    ValueError on any non-None value, pointing at the post-fit
-    ``results.aggregate()`` route).
+    after period detection. Aggregation is a post-fit step
+    (``results.aggregate()``); ``fit()`` takes no aggregation keyword.
     """
     # L_max is validated inline in fit() after period detection (needs
     # the period count). Not gated here.

diff_diff/diagnostic_report.py

@@ -807,7 +807,7 @@ def _resolve_event_study_surface(
 
         The raw ``event_study_effects`` field always takes precedence when it
         is not ``None`` — including the requested-but-empty ``{}`` sentinel,
-        which encodes fit-time configuration (e.g. an EfficientDiD
+        which encodes fit-time configuration (e.g. an ImputationDiD
         ``balance_e=`` that emptied the window) that a re-derivation without
         those settings would silently overturn. Derivation failures
         (bootstrapped fits, missing kits) are cached as skip reasons, never
@@ -1006,9 +1006,9 @@ def _instance_skip_reason(self, check: str) -> Optional[str]:
                             "per-event-time direct-effect output."
                         )
                     # StackedDiD (3.9, row M-024): the event-study surface
-                    # is ALWAYS computed, so ``aggregate='event_study'`` is
-                    # inert (and deprecated - it now warns). Zero estimated
-                    # pre-periods here means the event window has none:
+                    # is ALWAYS computed at fit (no aggregation keyword
+                    # exists since 4.0). Zero estimated pre-periods here
+                    # means the event window has none:
                     # the default ``kappa_pre=1`` grid minus the reference
                     # leaves no pre-period columns. Estimator-accurate
                     # remediation, per the SpilloverDiD precedent above.