Remove duplicated 'positivity' check?

Author: christopher-wild

causal_testing/testing/causal_test_case.py

@@ -98,14 +98,6 @@ def execute_test(self, estimator: type(Estimator), data_collector: Observational
 
         logger.info("treatments: %s", treatments)
         logger.info("outcomes: %s", outcome_variable)
-        minimal_adjustment_set = causal_specification.causal_dag.identification(BaseTestCase(treatment_variable, outcome_variable))
-        minimal_adjustment_set = minimal_adjustment_set - set(treatment_variable.name)
-        minimal_adjustment_set = minimal_adjustment_set - set(outcome_variable.name)
-
-        variables_for_positivity = list(minimal_adjustment_set) + [treatment_variable.name] + [outcome_variable.name]
-
-        if self._check_positivity_violation(variables_for_positivity, causal_specification.scenario, data_collector.data):
-            raise ValueError("POSITIVITY VIOLATION -- Cannot proceed.")
 
         causal_test_result = self._return_causal_test_results(estimator)
         return causal_test_result
@@ -130,32 +122,6 @@ def _return_causal_test_results(self, estimator):
 
         return causal_test_result
 
-    def _check_positivity_violation(self, variables_list, scenario: Scenario, df):
-        """Check whether the dataframe has a positivity violation relative to the specified variables list.
-
-        A positivity violation occurs when there is a stratum of the dataframe which does not have any data. Put simply,
-        if we split the dataframe into covariate sub-groups, each sub-group must contain both a treated and untreated
-        individual. If a positivity violation occurs, causal inference is still possible using a properly specified
-        parametric estimator. Therefore, we should not throw an exception upon violation but raise a warning instead.
-
-        :param variables_list: The list of variables for which positivity must be satisfied.
-        :return: True if positivity is violated, False otherwise.
-        """
-        if not (set(variables_list) - {x.name for x in scenario.hidden_variables()}).issubset(
-                df.columns
-        ):
-            missing_variables = set(variables_list) - set(df.columns)
-            logger.warning(
-                "Positivity violation: missing data for variables %s.\n"
-                "Causal inference is only valid if a well-specified parametric model is used.\n"
-                "Alternatively, consider restricting analysis to executions without the variables:"
-                ".",
-                missing_variables,
-            )
-            return True
-
-        return False
-
     def __str__(self):
         treatment_config = {self.treatment_variable.name: self.treatment_value}
         control_config = {self.treatment_variable.name: self.control_value}
@@ -164,3 +130,4 @@ def __str__(self):
             f"Running {treatment_config} instead of {control_config} should cause the following "
             f"changes to {outcome_variable}: {self.expected_causal_effect}."
         )
+