black

Author: jmafoster1

causal_testing/json_front/json_class.py

@@ -286,7 +286,7 @@ def _setup_test(
             "outcome": causal_test_case.outcome_variable.name,
             "df": causal_test_engine.scenario_execution_data_df,
             "effect_modifiers": causal_test_case.effect_modifier_configuration,
-            "alpha": test['alpha'] if 'alpha' in test else 0.05
+            "alpha": test["alpha"] if "alpha" in test else 0.05,
         }
         if "formula" in test:
             estimator_kwargs["formula"] = test["formula"]

causal_testing/testing/causal_test_outcome.py

@@ -53,7 +53,10 @@ def apply(self, res: CausalTestResult) -> bool:
             value = res.test_value.value if isinstance(res.ci_high(), Iterable) else [res.test_value.value]
 
             if not all(ci_low < 0 < ci_high for ci_low, ci_high in zip(ci_low, ci_high)):
-                print("FAILING ON", [(ci_low, ci_high) for ci_low, ci_high in zip(ci_low, ci_high) if not ci_low < 0 < ci_high])
+                print(
+                    "FAILING ON",
+                    [(ci_low, ci_high) for ci_low, ci_high in zip(ci_low, ci_high) if not ci_low < 0 < ci_high],
+                )
 
             return all(ci_low < 0 < ci_high for ci_low, ci_high in zip(ci_low, ci_high)) or all(
                 abs(v) < self.atol for v in value

causal_testing/testing/estimators.py

@@ -49,7 +49,7 @@ def __init__(
         outcome: str,
         df: pd.DataFrame = None,
         effect_modifiers: dict[str:Any] = None,
-        alpha: float = 0.05
+        alpha: float = 0.05,
     ):
         self.treatment = treatment
         self.treatment_value = treatment_value
@@ -303,9 +303,11 @@ def __init__(
         df: pd.DataFrame = None,
         effect_modifiers: dict[Variable:Any] = None,
         formula: str = None,
-        alpha: float = 0.05
+        alpha: float = 0.05,
     ):
-        super().__init__(treatment, treatment_value, control_value, adjustment_set, outcome, df, effect_modifiers, alpha=alpha)
+        super().__init__(
+            treatment, treatment_value, control_value, adjustment_set, outcome, df, effect_modifiers, alpha=alpha
+        )
 
         self.model = None
         if effect_modifiers is None: