Release 3.1.2: SDID scale fix, convergence warnings, roadmap refresh; remove deprecated SyntheticDiD params

igerber · claude · igerber · commit 231dca936ad9 · 2026-04-18T13:37:25.000-04:00
Package four merged PRs (#312 SDID catastrophic cancellation at extreme Y scale, #313 roadmap refresh, #314 FE imputation non-convergence signaling, #315 Frank-Wolfe SC weight solver non-convergence signaling) as 3.1.2. Also remove the SyntheticDiD(lambda_reg=...) and SyntheticDiD(zeta=...) kwargs, which have been deprecated with DeprecationWarning since v2.3.1 (2026-02-10) in favor of zeta_omega / zeta_lambda; their warning messages announced removal in v3.1. Passing the old kwargs now raises TypeError at __init__ and ValueError: Unknown parameter at set_params. Internal ridge-regression helpers that accept a lambda_reg parameter (compute_synthetic_weights, rank_control_units, Rust FFI bindings) are unaffected. Version strings bumped in diff_diff/__init__.py, pyproject.toml, rust/Cargo.toml, and diff_diff/guides/llms-full.txt. CHANGELOG populated with Fixed / Changed / Removed sections and comparison-link footer. TODO.md's "Deprecated Code" entry removed now that the task is done. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -7,8 +7,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [3.1.2] - 2026-04-18
+
+### Fixed
+- **SyntheticDiD catastrophic cancellation at extreme Y scale** (PR #312) - the Frank-Wolfe weight solver lost precision when outcome magnitudes were very large or very small; results are now numerically stable across scales.
+- **Non-convergence signaling in FE imputation alternating-projection solvers** (PR #314) - `ImputationDiD`, `TwoStageDiD`, and shared `within_transform` now emit a `ConvergenceWarning` when the alternating-projection / weighted-demean loop exits without meeting the tolerance. `max_iter` and `tol` are documented on `within_transform`.
+- **Non-convergence signaling in SyntheticDiD Frank-Wolfe solver** (PR #315) - the numpy-path Frank-Wolfe SC weight solver now emits a `ConvergenceWarning` when the loop exits without meeting `min_decrease`. Wrapper-level and `max_iter=0` regression tests added.
+
 ### Changed
-- Refresh `ROADMAP.md` to drop top-level phase numbering and reflect shipped state through v3.1.1. Absorbs dCDH into the Current State estimator list; adds Recently Shipped summary; reorganizes open work as Shipping Next / Under Consideration / AI-Agent Track / Long-term. Updates `docs/business-strategy.md`, `docs/survey-roadmap.md`, `docs/practitioner_decision_tree.rst`, `docs/choosing_estimator.rst`, `docs/api/chaisemartin_dhaultfoeuille.rst`, `README.md`, and `diff_diff/guides/llms-full.txt` to remove stale phase-deferral language now that the deferred items have shipped.
+- Refresh `ROADMAP.md` to drop top-level phase numbering and reflect shipped state through v3.1.1 (PR #313). Absorbs dCDH into the Current State estimator list; adds Recently Shipped summary; reorganizes open work as Shipping Next / Under Consideration / AI-Agent Track / Long-term. Updates `docs/business-strategy.md`, `docs/survey-roadmap.md`, `docs/practitioner_decision_tree.rst`, `docs/choosing_estimator.rst`, `docs/api/chaisemartin_dhaultfoeuille.rst`, `README.md`, and `diff_diff/guides/llms-full.txt` to remove stale phase-deferral language now that the deferred items have shipped.
+
+### Removed
+- **`SyntheticDiD(lambda_reg=...)` and `SyntheticDiD(zeta=...)`** - deprecated since v2.3.1 (2026-02-10) in favor of `zeta_omega` / `zeta_lambda`, which match R `synthdid`'s unit-weight / time-weight split. Passing the old kwargs now raises `TypeError` at `__init__` and `ValueError: Unknown parameter` at `set_params`. Internal helpers that take a `lambda_reg` ridge parameter (`compute_synthetic_weights`, `rank_control_units`, Rust FFI bindings) are unaffected - they remain supported.
 
 ## [3.1.1] - 2026-04-16
 
@@ -1298,6 +1308,7 @@ for the full feature history leading to this release.
 [2.1.2]: https://github.com/igerber/diff-diff/compare/v2.1.1...v2.1.2
 [2.1.1]: https://github.com/igerber/diff-diff/compare/v2.1.0...v2.1.1
 [2.1.0]: https://github.com/igerber/diff-diff/compare/v2.0.3...v2.1.0
+[3.1.2]: https://github.com/igerber/diff-diff/compare/v3.1.1...v3.1.2
 [3.1.1]: https://github.com/igerber/diff-diff/compare/v3.1.0...v3.1.1
 [3.1.0]: https://github.com/igerber/diff-diff/compare/v3.0.2...v3.1.0
 [3.0.2]: https://github.com/igerber/diff-diff/compare/v3.0.1...v3.0.2
diff --git a/TODO.md b/TODO.md
@@ -118,16 +118,6 @@ Different estimators compute SEs differently. Consider unified interface.
 Mypy reports 0 errors. All mixin `attr-defined` errors resolved via
 `TYPE_CHECKING`-guarded method stubs in bootstrap mixin classes.
 
-## Deprecated Code
-
-Deprecated parameters still present for backward compatibility:
-
-- `lambda_reg` and `zeta` in `SyntheticDiD` (`synthetic_did.py`)
-  - Deprecated in favor of `zeta_omega`/`zeta_lambda` parameters
-  - Remove in v3.1
-
----
-
 ## Test Coverage
 
 **Note**: 21 visualization tests are skipped when matplotlib unavailable—this is expected.
diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py
@@ -231,7 +231,7 @@
 ETWFE = WooldridgeDiD
 DCDH = ChaisemartinDHaultfoeuille
 
-__version__ = "3.1.1"
+__version__ = "3.1.2"
 __all__ = [
     # Estimators
     "DifferenceInDifferences",
diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt
@@ -2,7 +2,7 @@
 
 > A Python library for Difference-in-Differences causal inference analysis. Provides sklearn-like estimators with statsmodels-style output for econometric analysis.
 
-- Version: 3.1.1
+- Version: 3.1.2
 - Repository: https://github.com/igerber/diff-diff
 - License: MIT
 - Dependencies: numpy, pandas, scipy (no statsmodels dependency)
diff --git a/diff_diff/synthetic_did.py b/diff_diff/synthetic_did.py
@@ -141,26 +141,7 @@ def __init__(
         variance_method: str = "placebo",
         n_bootstrap: int = 200,
         seed: Optional[int] = None,
-        # Deprecated — accepted for backward compat, ignored with warning
-        lambda_reg: Optional[float] = None,
-        zeta: Optional[float] = None,
     ):
-        if lambda_reg is not None:
-            warnings.warn(
-                "lambda_reg is deprecated and ignored. Regularization is now "
-                "auto-computed from data. Use zeta_omega to override unit weight "
-                "regularization. Will be removed in v3.1.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        if zeta is not None:
-            warnings.warn(
-                "zeta is deprecated and ignored. Use zeta_lambda to override "
-                "time weight regularization. Will be removed in v3.1.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
         super().__init__(robust=True, cluster=None, alpha=alpha)
         self.zeta_omega = zeta_omega
         self.zeta_lambda = zeta_lambda
@@ -1465,17 +1446,8 @@ def get_params(self) -> Dict[str, Any]:
 
     def set_params(self, **params) -> "SyntheticDiD":
         """Set estimator parameters."""
-        # Deprecated parameter names — emit warning and ignore
-        _deprecated = {"lambda_reg", "zeta"}
         for key, value in params.items():
-            if key in _deprecated:
-                warnings.warn(
-                    f"{key} is deprecated and ignored. Use zeta_omega/zeta_lambda "
-                    f"instead. Will be removed in v3.1.",
-                    DeprecationWarning,
-                    stacklevel=2,
-                )
-            elif hasattr(self, key):
+            if hasattr(self, key):
                 setattr(self, key, value)
             else:
                 raise ValueError(f"Unknown parameter: {key}")
diff --git a/pyproject.toml b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "diff-diff"
-version = "3.1.1"
+version = "3.1.2"
 description = "Difference-in-Differences causal inference with sklearn-like API. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies, parallel trends."
 readme = "README.md"
 license = "MIT"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "diff_diff_rust"
-version = "3.1.1"
+version = "3.1.2"
 edition = "2021"
 rust-version = "1.84"
 description = "Rust backend for diff-diff DiD library"
diff --git a/tests/test_estimators.py b/tests/test_estimators.py
@@ -2758,27 +2758,6 @@ def test_get_set_params(self):
         sdid.set_params(variance_method="bootstrap")
         assert sdid.variance_method == "bootstrap"
 
-    def test_deprecated_params(self):
-        """Test that old parameter names emit DeprecationWarning."""
-        import warnings as _warnings
-
-        with _warnings.catch_warnings(record=True) as w:
-            _warnings.simplefilter("always")
-            sdid = SyntheticDiD(lambda_reg=1.0, zeta=0.5)
-            dep_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
-            assert len(dep_warnings) == 2
-
-        # Deprecated params are ignored — auto-computed regularization is used
-        assert sdid.zeta_omega is None
-        assert sdid.zeta_lambda is None
-
-        # set_params with deprecated names also warns
-        with _warnings.catch_warnings(record=True) as w:
-            _warnings.simplefilter("always")
-            sdid.set_params(lambda_reg=2.0)
-            dep_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
-            assert len(dep_warnings) == 1
-
     def test_missing_unit_column(self, sdid_panel_data):
         """Test error when unit column is missing."""
         sdid = SyntheticDiD()
diff --git a/tests/test_methodology_sdid.py b/tests/test_methodology_sdid.py
@@ -1064,55 +1064,15 @@ def test_set_params_new_names(self):
         assert sdid.zeta_omega == 2.0
         assert sdid.zeta_lambda == 0.1
 
-    def test_set_params_deprecated_names_warn(self):
-        """set_params with old names should emit DeprecationWarning."""
-        sdid = SyntheticDiD()
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            sdid.set_params(lambda_reg=1.0)
-            dep_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
-            assert len(dep_warnings) == 1
-
     def test_set_params_unknown_raises(self):
         """set_params with unknown name should raise ValueError."""
         sdid = SyntheticDiD()
         with pytest.raises(ValueError, match="Unknown parameter"):
             sdid.set_params(nonexistent_param=1.0)
 
 
-class TestDeprecatedParams:
-    """Test deprecated parameter handling in __init__."""
-
-    def test_lambda_reg_warns(self):
-        """SyntheticDiD(lambda_reg=...) emits DeprecationWarning."""
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            sdid = SyntheticDiD(lambda_reg=0.1)
-            dep = [x for x in w if issubclass(x.category, DeprecationWarning)]
-            assert len(dep) == 1
-            assert "lambda_reg" in str(dep[0].message)
-
-        # Deprecated param is ignored — auto-computed used
-        assert sdid.zeta_omega is None
-
-    def test_zeta_warns(self):
-        """SyntheticDiD(zeta=...) emits DeprecationWarning."""
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            sdid = SyntheticDiD(zeta=2.0)
-            dep = [x for x in w if issubclass(x.category, DeprecationWarning)]
-            assert len(dep) == 1
-            assert "zeta" in str(dep[0].message)
-
-        assert sdid.zeta_lambda is None
-
-    def test_both_deprecated_params(self):
-        """Both deprecated params at once should emit two warnings."""
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            SyntheticDiD(lambda_reg=0.5, zeta=1.5)
-            dep = [x for x in w if issubclass(x.category, DeprecationWarning)]
-            assert len(dep) == 2
+class TestDefaultVarianceMethod:
+    """Default variance_method sanity."""
 
     def test_default_variance_method_is_placebo(self):
         """Default variance_method should be 'placebo' (matching R)."""
