Added the option for an initial individual

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,3 +1,9 @@
+## Summary
+What does this PR do?
+
+## Main file changes
+Summarise changes to main files to be reviewed.
+
 ## Checklist
 Before you mark your PR as ready for review, please ensure you have completed the following.
 

causal_testing/discovery/hill_climber_discovery.py

@@ -103,16 +103,17 @@ def evaluate_fitness(
         )
         return fitness_values, problem_edges
 
-    def discover(self) -> CausalDAG:
+    def discover(self, individual: CausalDAG = None) -> CausalDAG:
         """
         Discover the causal DAG.
 
         :returns: The inferred causal DAG.
         """
 
-        individual = CausalDAG(ignore_cycles=True)
-        individual.add_nodes_from(self.df.columns)
-        individual.add_edges_from(self.possible_edges)
+        if individual is None:
+            individual = CausalDAG(ignore_cycles=True)
+            individual.add_nodes_from(self.df.columns)
+            individual.add_edges_from(self.possible_edges)
         self.remove_cycles(individual)
         fitness_values, problem_edges = self.evaluate_fitness(individual)
 

causal_testing/estimation/linear_regression_estimator.py

@@ -155,7 +155,7 @@ def estimate_ate_calculated(self, df: pd.DataFrame) -> EffectEstimate:
         return EffectEstimate("ate", pd.Series(treatment_outcome["mean"] - control_outcome["mean"]), ci_low, ci_high)
 
     def _get_confidence_intervals(self, model, treatment):
-        confidence_intervals = model.conf_int(alpha=self.alpha, cols=None)
+        confidence_intervals = model.conf_int(alpha=self.alpha)
         ci_low, ci_high = (
             pd.Series(confidence_intervals[0].loc[treatment]),
             pd.Series(confidence_intervals[1].loc[treatment]),

pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
     "pandas>=2.1",
     "scikit_learn~=1.4",
     "scipy>=1.12.0,<=1.17.1",
-    "statsmodels~=0.14",
+    "statsmodels~=0.15",
     "tabulate~=0.9",
     "pydot>=2.0",
     "pygad~=3.3",

tests/discovery_tests/test_abstract_discovery.py

@@ -274,7 +274,7 @@ def test_evaluate_tests_inestimable(self):
                     "outcome": "completed",
                 },
                 {
-                    "result": TestOutcome.INESTIMABLE,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "completed",

tests/main_tests/test_ctf.py

@@ -197,14 +197,14 @@ def test_ctf_evaluate_dag_inestimable(self):
         expected = pd.Series(
             {
                 "FAIL": 1,
-                "FAIL_ci_high": 2,
+                "FAIL_ci_high": 1,
                 "FAIL_ci_low": 0,
-                "INESTIMABLE": 1,
-                "INESTIMABLE_ci_high": 1,
+                "INESTIMABLE": 0,
+                "INESTIMABLE_ci_high": 0,
                 "INESTIMABLE_ci_low": 0,
-                "PASS": 4,
-                "PASS_ci_high": 4,
-                "PASS_ci_low": 0,
+                "PASS": 5,
+                "PASS_ci_high": 5,
+                "PASS_ci_low": 2,
             }
         ).sort_index()
         pd.testing.assert_series_equal(results, expected)