Fix ValueError on read-only arrays in BaseSLearner.predict()

causalml/inference/meta/slearner.py

@@ -109,13 +109,14 @@ def predict(
         for group in self.t_groups:
             model = self.models[group]
 
-            # set the treatment column to zero (the control group)
-            X_new = np.hstack((np.zeros((X.shape[0], 1)), X))
-            yhat_cs[group] = model.predict(X_new)
+            # Build separate arrays for control and treatment to avoid in-place
+            # mutation, which fails when learners like CatBoost set the
+            # writeable flag to False on arrays passed to predict().
+            X_new_c = np.hstack((np.zeros((X.shape[0], 1)), X))
+            yhat_cs[group] = model.predict(X_new_c)
 
-            # set the treatment column to one (the treatment group)
-            X_new[:, 0] = 1
-            yhat_ts[group] = model.predict(X_new)
+            X_new_t = np.hstack((np.ones((X.shape[0], 1)), X))
+            yhat_ts[group] = model.predict(X_new_t)
 
             if (y is not None) and (treatment is not None) and verbose:
                 mask = (treatment == group) | (treatment == self.control_name)
@@ -346,13 +347,14 @@ def predict(
         for group in self.t_groups:
             model = self.models[group]
 
-            # set the treatment column to zero (the control group)
-            X_new = np.hstack((np.zeros((X.shape[0], 1)), X))
-            yhat_cs[group] = model.predict_proba(X_new)[:, 1]
+            # Build separate arrays for control and treatment to avoid in-place
+            # mutation, which fails when learners like CatBoost set the
+            # writeable flag to False on arrays passed to predict().
+            X_new_c = np.hstack((np.zeros((X.shape[0], 1)), X))
+            yhat_cs[group] = model.predict_proba(X_new_c)[:, 1]
 
-            # set the treatment column to one (the treatment group)
-            X_new[:, 0] = 1
-            yhat_ts[group] = model.predict_proba(X_new)[:, 1]
+            X_new_t = np.hstack((np.ones((X.shape[0], 1)), X))
+            yhat_ts[group] = model.predict_proba(X_new_t)[:, 1]
 
             if y is not None and (treatment is not None) and verbose:
                 mask = (treatment == group) | (treatment == self.control_name)

tests/test_meta_learners.py

@@ -38,6 +38,23 @@
 from .const import RANDOM_SEED, N_SAMPLE, ERROR_THRESHOLD, CONTROL_NAME, CONVERSION
 
 
+class ReadOnlyLinearRegression:
+    """Minimal regressor that marks input arrays read-only like CatBoost."""
+
+    def __init__(self):
+        self.model = LinearRegression()
+
+    def fit(self, X, y):
+        self.model.fit(X, y)
+        X.flags.writeable = False
+        return self
+
+    def predict(self, X):
+        result = self.model.predict(X)
+        X.flags.writeable = False
+        return result
+
+
 def test_synthetic_data():
     y, X, treatment, tau, b, e = synthetic_data(mode=1, n=N_SAMPLE, p=8, sigma=0.1)
 
@@ -97,6 +114,21 @@ def test_BaseSLearner(generate_regression_data):
     assert (ate_p_pt == ate_p) and (lb_pt == lb) and (ub_pt == ub)
 
 
+def test_BaseSLearner_predict_with_readonly_arrays(generate_regression_data):
+    y, X, treatment, _, _, _ = generate_regression_data()
+    X_readonly = np.array(X, copy=True)
+    X_readonly.flags.writeable = False
+
+    learner = BaseSLearner(learner=ReadOnlyLinearRegression())
+
+    # Exercise both fit() and predict() with read-only array behavior.
+    learner.fit(X=X_readonly, treatment=treatment, y=y)
+    cate = learner.predict(X=X_readonly)
+
+    assert cate.shape == (X.shape[0], 1)
+    assert not X_readonly.flags.writeable
+
+
 def test_BaseSRegressor(generate_regression_data):
     y, X, treatment, tau, b, e = generate_regression_data()