Merge pull request #269 from CITCOM-project/interaction-terms

Temporary workaround for "I(...) not in df" bug

causal_testing/specification/causal_dag.py

@@ -133,8 +133,8 @@ class CausalDAG(nx.DiGraph):
     def __init__(self, dot_path: str = None, **attr):
         super().__init__(**attr)
         if dot_path:
-            with open(dot_path, 'r', encoding='utf-8') as file:
-                dot_content = file.read().replace('\n', '')
+            with open(dot_path, "r", encoding="utf-8") as file:
+                dot_content = file.read().replace("\n", "")
             # Previously, we used pydot_graph_from_file() to read in the dot_path directly, however,
             # this method does not currently have a way of removing spurious nodes.
             # Workaround: Read in the file using open(), remove new lines, and then create the pydot_graph.

causal_testing/surrogate/causal_surrogate_assisted.py

@@ -19,7 +19,7 @@ class SimulationResult:
     relationship: str
 
 
-class SearchAlgorithm(ABC): # pylint: disable=too-few-public-methods
+class SearchAlgorithm(ABC):  # pylint: disable=too-few-public-methods
     """Class to be inherited with the search algorithm consisting of a search function and the fitness function of the
     space to be searched"""
 

causal_testing/surrogate/surrogate_search_algorithms.py

@@ -26,7 +26,7 @@ def __init__(self, delta=0.05, config: dict = None) -> None:
 
     # pylint: disable=too-many-locals
     def search(
-            self, surrogate_models: list[CubicSplineRegressionEstimator], specification: CausalSpecification
+        self, surrogate_models: list[CubicSplineRegressionEstimator], specification: CausalSpecification
     ) -> list:
         solutions = []
 
@@ -47,7 +47,8 @@ def fitness_function(ga, solution, idx):  # pylint: disable=unused-argument
                 ate = surrogate.estimate_ate_calculated(adjustment_dict)
                 if len(ate) > 1:
                     raise ValueError(
-                        "Multiple ate values provided but currently only single values supported in this method")
+                        "Multiple ate values provided but currently only single values supported in this method"
+                    )
                 return contradiction_function(ate[0])
 
             gene_types, gene_space = self.create_gene_types(surrogate, specification)
@@ -84,7 +85,7 @@ def fitness_function(ga, solution, idx):  # pylint: disable=unused-argument
 
     @staticmethod
     def create_gene_types(
-            surrogate_model: CubicSplineRegressionEstimator, specification: CausalSpecification
+        surrogate_model: CubicSplineRegressionEstimator, specification: CausalSpecification
     ) -> tuple[list, list]:
         """Generate the gene_types and gene_space for a given fitness function and specification
         :param surrogate_model: Instance of a CubicSplineRegressionEstimator

causal_testing/testing/causal_test_outcome.py

@@ -29,10 +29,12 @@ class SomeEffect(CausalTestOutcome):
     def apply(self, res: CausalTestResult) -> bool:
         if res.test_value.type == "risk_ratio":
             return any(
-                1 < ci_low < ci_high or ci_low < ci_high < 1 for ci_low, ci_high in zip(res.ci_low(), res.ci_high()))
-        if res.test_value.type in ('coefficient', 'ate'):
+                1 < ci_low < ci_high or ci_low < ci_high < 1 for ci_low, ci_high in zip(res.ci_low(), res.ci_high())
+            )
+        if res.test_value.type in ("coefficient", "ate"):
             return any(
-                0 < ci_low < ci_high or ci_low < ci_high < 0 for ci_low, ci_high in zip(res.ci_low(), res.ci_high()))
+                0 < ci_low < ci_high or ci_low < ci_high < 0 for ci_low, ci_high in zip(res.ci_low(), res.ci_high())
+            )
 
         raise ValueError(f"Test Value type {res.test_value.type} is not valid for this TestOutcome")
 
@@ -51,17 +53,19 @@ def __init__(self, atol: float = 1e-10, ctol: float = 0.05):
 
     def apply(self, res: CausalTestResult) -> bool:
         if res.test_value.type == "risk_ratio":
-            return any(ci_low < 1 < ci_high or np.isclose(value, 1.0, atol=self.atol) for ci_low, ci_high, value in
-                       zip(res.ci_low(), res.ci_high(), res.test_value.value))
-        if res.test_value.type in ('coefficient', 'ate'):
+            return any(
+                ci_low < 1 < ci_high or np.isclose(value, 1.0, atol=self.atol)
+                for ci_low, ci_high, value in zip(res.ci_low(), res.ci_high(), res.test_value.value)
+            )
+        if res.test_value.type in ("coefficient", "ate"):
             value = res.test_value.value if isinstance(res.ci_high(), Iterable) else [res.test_value.value]
             return (
-                    sum(
-                        not ((ci_low < 0 < ci_high) or abs(v) < self.atol)
-                        for ci_low, ci_high, v in zip(res.ci_low(), res.ci_high(), value)
-                    )
-                    / len(value)
-                    < self.ctol
+                sum(
+                    not ((ci_low < 0 < ci_high) or abs(v) < self.atol)
+                    for ci_low, ci_high, v in zip(res.ci_low(), res.ci_high(), value)
+                )
+                / len(value)
+                < self.ctol
             )
 
         raise ValueError(f"Test Value type {res.test_value.type} is not valid for this TestOutcome")

causal_testing/testing/estimators.py

@@ -352,7 +352,14 @@ def estimate_coefficient(self) -> tuple[pd.Series, list[pd.Series, pd.Series]]:
         model = self._run_linear_regression()
         newline = "\n"
         patsy_md = ModelDesc.from_formula(self.treatment)
-        if any((self.df.dtypes[factor.name()] == 'object' for factor in patsy_md.rhs_termlist[1].factors)):
+        if any(
+            (
+                self.df.dtypes[factor.name()] == "object"
+                for factor in patsy_md.rhs_termlist[1].factors
+                # We want to remove this long term as it prevents us from discovering categoricals within I(...) blocks
+                if factor.name() in self.df.dtypes
+            )
+        ):
             design_info = dmatrix(self.formula.split("~")[1], self.df).design_info
             treatment = design_info.column_names[design_info.term_name_slices[self.treatment]]
         else: