Merge branch 'main' into estimator_params_for_linear

causal_testing/testing/causal_test_case.py

@@ -57,22 +57,6 @@ def __init__(
         else:
             self.effect_modifier_configuration = {}
 
-    def get_treatment_variable(self):
-        """Return the treatment variable name (as string) for this causal test case"""
-        return self.treatment_variable.name
-
-    def get_outcome_variable(self):
-        """Return the outcome variable name (as string) for this causal test case."""
-        return self.outcome_variable.name
-
-    def get_control_value(self):
-        """Return a the control value of the treatment variable in this causal test case."""
-        return self.control_value
-
-    def get_treatment_value(self):
-        """Return the treatment value of the treatment variable in this causal test case."""
-        return self.treatment_value
-
     def execute_test(self, estimator: type(Estimator), data_collector: DataCollector) -> CausalTestResult:
         """Execute a causal test case and return the causal test result.
 

causal_testing/testing/causal_test_result.py

@@ -85,9 +85,11 @@ def to_dict(self, json=False):
             "outcome": self.estimator.outcome,
             "adjustment_set": list(self.adjustment_set) if json else self.adjustment_set,
             "effect_measure": self.test_value.type,
-            "effect_estimate": self.test_value.value,
-            "ci_low": self.ci_low(),
-            "ci_high": self.ci_high(),
+            "effect_estimate": self.test_value.value.to_dict()
+            if json and hasattr(self.test_value.value, "to_dict")
+            else self.test_value.value,
+            "ci_low": self.ci_low().to_dict() if json and hasattr(self.ci_low(), "to_dict") else self.ci_low(),
+            "ci_high": self.ci_high().to_dict() if json and hasattr(self.ci_high(), "to_dict") else self.ci_high(),
         }
         if self.adequacy:
             base_dict["adequacy"] = self.adequacy.to_dict()

examples/poisson-line-process/example_poisson_process.py

@@ -90,8 +90,8 @@ def causal_test_intensity_num_shapes(
     # 8. Set up an estimator
     data = pd.read_csv(observational_data_path)
 
-    treatment = causal_test_case.get_treatment_variable()
-    outcome = causal_test_case.get_outcome_variable()
+    treatment = causal_test_case.treatment_variable.name
+    outcome = causal_test_case.outcome_variable.name
 
     estimator = None
     if empirical:

pyproject.toml

@@ -20,7 +20,7 @@ dependencies = [
     "fitter~=1.4",
     "lhsmdu~=1.1",
     "networkx~=2.6",
-    "numpy~=1.22.0",
+    "numpy~=1.23",
     "pandas~=1.3",
     "scikit_learn~=1.1",
     "scipy~=1.7",

tests/testing_tests/test_causal_test_case.py

@@ -37,18 +37,6 @@ def setUp(self) -> None:
             treatment_value=1,
         )
 
-    def test_get_treatment_variable(self):
-        self.assertEqual(self.causal_test_case.get_treatment_variable(), "A")
-
-    def test_get_outcome_variable(self):
-        self.assertEqual(self.causal_test_case.get_outcome_variable(), "C")
-
-    def test_get_treatment_value(self):
-        self.assertEqual(self.causal_test_case.get_treatment_value(), 1)
-
-    def test_get_control_value(self):
-        self.assertEqual(self.causal_test_case.get_control_value(), 0)
-
     def test_str(self):
         self.assertEqual(
             str(self.causal_test_case),