Add validation to _data_setter for non-standard data node names (#847)

* Rebase onto main and add happy-path validation test

- Resolved merge conflicts in all three files against origin/main
- Restored upstream class docstring rubric (Parameters + Examples) in
  PyMCModel
- Restored actionable error message pointing to
  BayesianBasisExpansionTimeSeries
- Added test_standard_data_nodes_predict exercising the validation
  happy path to satisfy codecov/patch

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

* Rebase onto main preserving all upstream classes and PR validation

Previous stash-based resolution accidentally dropped ~330 lines from
pymc_models.py, removing SyntheticDifferenceInDifferencesWeightFitter and
other classes added to main after the branch diverged.

This commit takes origin/main's full pymc_models.py (2652 lines) and
re-applies only the PR's changes: class docstring note, _data_setter
validation, method docstring update, and the codecov happy-path test.

Also adds test_standard_data_nodes_predict to cover the validation
loop's non-error path, resolving the codecov/patch gap.

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

* Use origin/main panel_regression.py (no PR changes needed here)

* Remove unused import (ruff fix)

---------

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Author: drbenvincent

causalpy/tests/test_pymc_models.py

@@ -270,6 +270,30 @@ def build_model(self, X, y, coords):
         with pytest.raises(ValueError, match="Data node 'y' not found"):
             model.predict(X=X)
 
+    def test_standard_data_nodes_predict(self, rng, mock_pymc_sample):
+        """Standard model with 'X' and 'y' data nodes exercises validation happy path."""
+        from causalpy.pymc_models import LinearRegression
+
+        X = xr.DataArray(
+            rng.normal(size=(10, 1)),
+            dims=["obs_ind", "coeffs"],
+            coords={"obs_ind": np.arange(10), "coeffs": ["x1"]},
+        )
+        y = xr.DataArray(
+            rng.normal(size=(10, 1)),
+            dims=["obs_ind", "treated_units"],
+            coords={"obs_ind": np.arange(10), "treated_units": ["unit_0"]},
+        )
+        coords = {
+            "obs_ind": np.arange(10),
+            "coeffs": ["x1"],
+            "treated_units": ["unit_0"],
+        }
+        model = LinearRegression(sample_kwargs={"chains": 2, "draws": 2})
+        model.fit(X, y, coords=coords)
+        predictions = model.predict(X=X)
+        assert isinstance(predictions, az.InferenceData)
+
 
 def test_idata_property(mock_pymc_sample, did_data):
     """Test that we can access the idata property of the model"""