Support datetime treatment_time in SyntheticControl plots (#731)

* Support datetime treatment_time in SyntheticControl plots.

Convert intervention line positions through each axis unit converter so both numeric and datetime indices render correctly, and add an sklearn integration regression test for datetime treatment_time plotting.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Add fallback coverage test for SyntheticControl axis conversion.

Exercise the error-handling path in _convert_treatment_time_for_axis for TypeError and ValueError so the synthetic_control.py diff reaches Codecov patch coverage requirements.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>

Author: drbenvincent

causalpy/experiments/synthetic_control.py

@@ -281,6 +281,18 @@ def summary(self, round_to: int | None = None) -> None:
             print(f"Treated unit: {self.treated_units[0]}")
         self.print_coefficients(round_to)
 
+    @staticmethod
+    def _convert_treatment_time_for_axis(
+        axis: plt.Axes, treatment_time: int | float | pd.Timestamp
+    ) -> int | float | pd.Timestamp:
+        """
+        Convert treatment time into the plotting units expected by a specific axis.
+        """
+        try:
+            return axis.xaxis.convert_units(treatment_time)
+        except (TypeError, ValueError):
+            return treatment_time
+
     def _bayesian_plot(
         self,
         round_to: int | None = None,
@@ -402,8 +414,11 @@ def _bayesian_plot(
 
         # Intervention line
         for i in [0, 1, 2]:
+            treatment_time = self._convert_treatment_time_for_axis(
+                ax[i], self.treatment_time
+            )
             ax[i].axvline(
-                x=self.treatment_time,
+                x=treatment_time,
                 ls="-",
                 lw=3,
                 color="r",
@@ -528,10 +543,13 @@ def _ols_plot(
             label="Causal impact",
         )
 
-        # Intervention line (see #725 for datetime treatment_time support)
+        # Intervention line
         for i in [0, 1, 2]:
+            treatment_time = self._convert_treatment_time_for_axis(
+                ax[i], self.treatment_time
+            )
             ax[i].axvline(
-                x=self.treatment_time,
+                x=treatment_time,
                 ls="-",
                 lw=3,
                 color="r",

causalpy/tests/test_integration_skl_examples.py

@@ -174,6 +174,50 @@ def test_sc():
     )
 
 
+@pytest.mark.integration
+def test_sc_datetime_treatment_time_plot():
+    """Test SyntheticControl plotting with datetime treatment_time and sklearn model."""
+    df = (
+        cp.load_data("geolift1")
+        .assign(time=lambda x: pd.to_datetime(x["time"]))
+        .set_index("time")
+    )
+    treatment_time = pd.to_datetime("2022-01-01")
+
+    result = cp.SyntheticControl(
+        df,
+        treatment_time,
+        control_units=["Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus"],
+        treated_units=["Denmark"],
+        model=cp.skl_models.WeightedProportion(),
+    )
+
+    fig, ax = result.plot()
+    assert isinstance(fig, plt.Figure)
+    assert isinstance(ax, np.ndarray) and all(
+        isinstance(item, plt.Axes) for item in ax
+    ), "ax must be a numpy.ndarray of plt.Axes"
+
+
+@pytest.mark.parametrize("error_type", [TypeError, ValueError])
+def test_sc_convert_treatment_time_for_axis_fallback(error_type):
+    """Return original treatment_time when axis conversion raises."""
+
+    class FailingXAxis:
+        def convert_units(self, _value):
+            raise error_type("conversion failed")
+
+    class FailingAxis:
+        xaxis = FailingXAxis()
+
+    treatment_time = pd.Timestamp("2022-01-01")
+    converted = cp.SyntheticControl._convert_treatment_time_for_axis(
+        FailingAxis(), treatment_time
+    )
+
+    assert converted is treatment_time
+
+
 @pytest.mark.integration
 def test_rd_linear_main_effects():
     """