Removed adequacy warning catching and made alpha an MR parameter

causal_testing/testing/causal_test_adequacy.py

@@ -7,8 +7,6 @@
 from itertools import combinations
 
 import pandas as pd
-from lifelines.exceptions import ConvergenceError
-from numpy.linalg import LinAlgError
 
 from causal_testing.specification.causal_dag import CausalDAG
 from causal_testing.testing.causal_test_case import CausalTestCase
@@ -104,21 +102,9 @@ def measure_adequacy(self):
                 estimator.df = estimator.df[estimator.df[self.group_by].isin(ids)]
             else:
                 estimator.df = estimator.df.sample(len(estimator.df), replace=True, random_state=i)
-            try:
-                result = self.test_case.execute_test(estimator)
-                outcomes.append(self.test_case.expected_causal_effect.apply(result))
-                results.append(result.effect_estimate.to_df())
-            except LinAlgError:
-                logger.warning("Adequacy LinAlgError")
-                continue
-            except ConvergenceError:
-                logger.warning("Adequacy ConvergenceError")
-                continue
-            except ValueError as e:
-                logger.warning(f"Adequacy ValueError: {e}")
-                continue
-        # outcomes = [self.test_case.expected_causal_effect.apply(c) for c in results]
-        # results = pd.concat([c.effect_estimate.to_df() for c in results])
+            result = self.test_case.execute_test(estimator)
+            outcomes.append(self.test_case.expected_causal_effect.apply(result))
+            results.append(result.effect_estimate.to_df())
         results = pd.concat(results)
         results["var"] = results.index
         results["passed"] = outcomes

causal_testing/testing/metamorphic_relation.py

@@ -33,38 +33,62 @@ def __eq__(self, other):
         same_adjustment_set = set(self.adjustment_vars) == set(other.adjustment_vars)
         return same_type and same_treatment and same_outcome and same_effect and same_adjustment_set
 
-
-class ShouldCause(MetamorphicRelation):
-    """Class representing a should cause metamorphic relation."""
-
     def to_json_stub(
         self,
         skip: bool = False,
         estimate_type: str = "coefficient",
         effect_type: str = "direct",
         estimator: str = "LinearRegressionEstimator",
+        alpha: float = 0.05,
     ) -> dict:
         """
         Convert to a JSON frontend stub string for user customisation.
         :param skip: Whether to skip the test (default False).
         :param effect_type: The type of causal effect to consider (total or direct)
         :param estimate_type: The estimate type to use when evaluating tests
         :param estimator: The name of the estimator class to use when evaluating the test
+        :param alpha: The significance level to use when calculating the confidence intervals
         """
         return {
             "name": str(self),
             "estimator": estimator,
             "estimate_type": estimate_type,
             "effect": effect_type,
             "treatment_variable": self.base_test_case.treatment_variable,
-            "expected_effect": {self.base_test_case.outcome_variable: "SomeEffect"},
             "formula": (
                 f"{self.base_test_case.outcome_variable} ~ "
                 f"{' + '.join([self.base_test_case.treatment_variable] + self.adjustment_vars)}"
             ),
+            "alpha": alpha,
             "skip": skip,
         }
 
+
+class ShouldCause(MetamorphicRelation):
+    """Class representing a should cause metamorphic relation."""
+
+    def to_json_stub(
+        self,
+        skip: bool = False,
+        estimate_type: str = "coefficient",
+        effect_type: str = "direct",
+        estimator: str = "LinearRegressionEstimator",
+        alpha: float = 0.05,
+    ) -> dict:
+        """
+        Convert to a JSON frontend stub string for user customisation.
+        :param skip: Whether to skip the test (default False).
+        :param effect_type: The type of causal effect to consider (total or direct)
+        :param estimate_type: The estimate type to use when evaluating tests
+        :param estimator: The name of the estimator class to use when evaluating the test
+        :param alpha: The significance level to use when calculating the confidence intervals
+        """
+        return super().to_json_stub(
+            skip=skip, estimate_type=estimate_type, effect_type=effect_type, estimator=estimator, alpha=alpha
+        ) | {
+            "expected_effect": {self.base_test_case.outcome_variable: "SomeEffect"},
+        }
+
     def __str__(self):
         formatted_str = f"{self.base_test_case.treatment_variable} --> {self.base_test_case.outcome_variable}"
         if self.adjustment_vars:
@@ -81,27 +105,20 @@ def to_json_stub(
         estimate_type: str = "coefficient",
         effect_type: str = "direct",
         estimator: str = "LinearRegressionEstimator",
+        alpha: float = 0.05,
     ) -> dict:
         """
         Convert to a JSON frontend stub string for user customisation.
         :param skip: Whether to skip the test (default False).
         :param effect_type: The type of causal effect to consider (total or direct)
         :param estimate_type: The estimate type to use when evaluating tests
         :param estimator: The name of the estimator class to use when evaluating the test
+        :param alpha: The significance level to use when calculating the confidence intervals
         """
-        return {
-            "name": str(self),
-            "estimator": estimator,
-            "estimate_type": estimate_type,
-            "effect": effect_type,
-            "treatment_variable": self.base_test_case.treatment_variable,
+        return super().to_json_stub(
+            skip=skip, estimate_type=estimate_type, effect_type=effect_type, estimator=estimator, alpha=alpha
+        ) | {
             "expected_effect": {self.base_test_case.outcome_variable: "NoEffect"},
-            "formula": (
-                f"{self.base_test_case.outcome_variable} ~ "
-                f"{' + '.join([self.base_test_case.treatment_variable] + self.adjustment_vars)}"
-            ),
-            "alpha": 0.05,
-            "skip": skip,
         }
 
     def __str__(self):

pyproject.toml

@@ -18,7 +18,7 @@ dependencies = [
 "lifelines~=0.30.0",
 "networkx>=3.4,<3.5",
 "numpy>=1.26.0,<=2.2.0",
-"pandas>=2.1",
+"pandas>=2.1,<3",
 "scikit_learn~=1.4",
 "scipy>=1.12.0,<=1.16.2",
 "statsmodels~=0.14",
@@ -100,8 +100,12 @@ skip_missing_interpreters = false # fail if devs don’t have all required Pytho
 description = "Run pytest under {base_python}"
 extras = ["dev","test"]
 deps = ["pytest"]
-commands = [["pytest"]]
-
+commands = [
+  [
+    "pytest",
+    "{posargs:tests}",
+  ],
+]
 # Automatically test for type-checking (TODO: enable type checking in env_list in the future)
 [tool.tox.env.type]
 description = "Run type checks with mypy on the codebase"

tests/testing_tests/test_metamorphic_relations.py

@@ -104,6 +104,7 @@ def test_should_cause_json_stub(self):
                 "formula": "Z ~ X1",
                 "treatment_variable": "X1",
                 "name": "X1 --> Z",
+                "alpha": 0.05,
                 "skip": False,
             },
         )
@@ -130,6 +131,7 @@ def test_should_cause_logistic_json_stub(self):
                 "formula": "Z ~ X1",
                 "treatment_variable": "X1",
                 "name": "X1 --> Z",
+                "alpha": 0.05,
                 "skip": False,
             },
         )