Update docs and reuse fixture per PR review feedback

- Clarify reference_period docstring: explicit triggers normalization,
  auto-inferred only applies hollow marker styling
- Update Notes section to distinguish explicit vs inferred behavior
- Refactor two tests to reuse cs_results fixture instead of re-fitting

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: igerber

diff_diff/visualization.py

@@ -73,8 +73,10 @@ def plot_event_study(
     periods : list, optional
         List of periods to plot. If None, uses all periods from results.
     reference_period : any, optional
-        The reference period (normalized to effect=0). Will be shown as a
-        hollow marker. If None, tries to infer from results.
+        The reference period to highlight. When explicitly provided, effects
+        are normalized (ref effect subtracted) and ref SE is set to NaN.
+        When None and auto-inferred from results, only hollow marker styling
+        is applied (no normalization). If None, tries to infer from results.
     pre_periods : list, optional
         List of pre-treatment periods. Used for shading.
     post_periods : list, optional
@@ -151,8 +153,9 @@ def plot_event_study(
        trends holds. Large pre-treatment effects suggest the assumption may
        be violated.
 
-    2. **Reference period**: Usually the last pre-treatment period (t=-1),
-       normalized to zero. This is the omitted category.
+    2. **Reference period**: Usually the last pre-treatment period (t=-1).
+       When explicitly specified via ``reference_period``, effects are normalized
+       to zero at this period. When auto-inferred, shown with hollow marker only.
 
     3. **Post-treatment periods**: The treatment effects of interest. These
        show how the outcome evolved after treatment.

tests/test_visualization.py

@@ -456,7 +456,7 @@ def test_plot_event_study_normalization_with_nan_reference(self):
 
         plt.close()
 
-    def test_plot_cs_results_no_auto_normalization(self):
+    def test_plot_cs_results_no_auto_normalization(self, cs_results):
         """Test that auto-inferred reference period does NOT normalize effects.
 
         When CallawaySantAnna results auto-infer reference_period=-1 (or from n_groups=0),
@@ -466,17 +466,8 @@ def test_plot_cs_results_no_auto_normalization(self):
         pytest.importorskip("matplotlib")
         import matplotlib.pyplot as plt
 
-        # Generate staggered data and fit CallawaySantAnna with event study
-        data = generate_staggered_data()
-        cs = CallawaySantAnna()
-        results = cs.fit(
-            data,
-            outcome='outcome',
-            unit='unit',
-            time='time',
-            first_treat='first_treat',
-            aggregate='event_study'
-        )
+        # Use fixture instead of re-fitting
+        results = cs_results
 
         # Get original effects from results (before any normalization)
         original_effects = {
@@ -513,7 +504,7 @@ def test_plot_cs_results_no_auto_normalization(self):
 
         plt.close()
 
-    def test_plot_cs_results_explicit_reference_normalizes(self):
+    def test_plot_cs_results_explicit_reference_normalizes(self, cs_results):
         """Test that explicit reference_period normalizes CallawaySantAnna results.
 
         When user explicitly passes reference_period=X to plot_event_study,
@@ -522,17 +513,8 @@ def test_plot_cs_results_explicit_reference_normalizes(self):
         pytest.importorskip("matplotlib")
         import matplotlib.pyplot as plt
 
-        # Generate staggered data and fit CallawaySantAnna with event study
-        data = generate_staggered_data()
-        cs = CallawaySantAnna()
-        results = cs.fit(
-            data,
-            outcome='outcome',
-            unit='unit',
-            time='time',
-            first_treat='first_treat',
-            aggregate='event_study'
-        )
+        # Use fixture instead of re-fitting
+        results = cs_results
 
         # Get original effects from results
         original_effects = {