Small correction of average p-value merger

Before, a single p-value still got adjusted using the 2 * ... formula, which led to an unnecessary adjustment.

Signed-off-by: Patrick Bloebaum <[email protected]>

dowhy/gcm/stats.py

@@ -25,6 +25,8 @@ def merge_p_values_average(p_values: Union[np.ndarray, List[float]], randomizati
     """
     if len(p_values) == 0:
         raise ValueError("Given list of p-values is empty!")
+    if len(p_values) == 1:
+        return p_values[0]
 
     if np.all(np.isnan(p_values)):
         return float(np.nan)

tests/gcm/test_stats.py

@@ -73,6 +73,7 @@ def test_given_invalid_inputs_when_merge_p_values_quantile_then_raises_error():
 def test_when_merge_p_values_average_without_randomization_then_returns_expected_results():
     assert merge_p_values_average([0]) == 0
     assert merge_p_values_average([1]) == 1
+    assert merge_p_values_average([0.3]) == 0.3
     assert merge_p_values_average([0, 1]) == approx(1.0)
     assert merge_p_values_average([0, 0, 1]) == 0
     assert merge_p_values_average([0, 0.5, 0.5, np.nan, 1, np.nan]) == approx(1.0)
@@ -83,6 +84,7 @@ def test_when_merge_p_values_average_without_randomization_then_returns_expected
 def test_when_merge_p_values_average_with_randomization_then_returns_expected_results():
     assert merge_p_values_average([0], randomization=True) == 0
     assert merge_p_values_average([1], randomization=True) == 1
+    assert merge_p_values_average([0.3], randomization=True) == 0.3
     assert merge_p_values_average([0, 1], randomization=True) == approx(0.0, abs=0.01)
     assert merge_p_values_average([0, 0, 1], randomization=True) == approx(0.0, abs=0.01)
     assert merge_p_values_average([0, np.nan, 0, np.nan, 1, 1], randomization=True) == approx(0.0, abs=0.01)