Add: support for predict_proba for estimators that support it

Author: arnauddhaene

econml/metalearners/_metalearners.py

@@ -109,7 +109,7 @@ def fit(self, Y, T, *, X, inference=None):
             self.models[ind].fit(X[T == ind], Y[T == ind])
 
     def const_marginal_effect(self, X):
-        """Calculate the constant marignal treatment effect on a vector of features for each sample.
+        """Calculate the constant marginal treatment effect on a vector of features for each sample.
 
         Parameters
         ----------
@@ -127,7 +127,11 @@ def const_marginal_effect(self, X):
         X = check_array(X)
         taus = []
         for ind in range(self._d_t[0]):
-            taus.append(self.models[ind + 1].predict(X) - self.models[0].predict(X))
+            if hasattr(self.models[ind + 1], 'predict_proba'):
+                taus.append(self.models[ind + 1].predict_proba(X)[:, 1] - self.models[0].predict_proba(X)[:, 1])
+            else:
+                taus.append(self.models[ind + 1].predict(X) - self.models[0].predict(X))
+
         taus = np.column_stack(taus).reshape((-1,) + self._d_t + self._d_y)  # shape as of m*d_t*d_y
         if self._d_y:
             taus = transpose(taus, (0, 2, 1))  # shape as of m*d_y*d_t
@@ -242,7 +246,12 @@ def const_marginal_effect(self, X=None):
         X = check_array(X)
         Xs, Ts = broadcast_unit_treatments(X, self._d_t[0] + 1)
         feat_arr = np.concatenate((Xs, Ts), axis=1)
-        prediction = self.overall_model.predict(feat_arr).reshape((-1, self._d_t[0] + 1,) + self._d_y)
+
+        if hasattr(self.overall_model, 'predict_proba'):
+            prediction = self.overall_model.predict_proba(feat_arr)[:, 1].reshape((-1, self._d_t[0] + 1,) + self._d_y)
+        else:
+            prediction = self.overall_model.predict(feat_arr).reshape((-1, self._d_t[0] + 1,) + self._d_y)
+
         if self._d_y:
             prediction = transpose(prediction, (0, 2, 1))
             taus = (prediction - np.repeat(prediction[:, :, 0], self._d_t[0] + 1).reshape(prediction.shape))[:, :, 1:]
@@ -393,8 +402,14 @@ def const_marginal_effect(self, X):
         taus = []
         for ind in range(self._d_t[0]):
             propensity_scores = self.propensity_models[ind].predict_proba(X)[:, 1:]
-            tau_hat = propensity_scores * self.cate_controls_models[ind].predict(X).reshape(m, -1) \
-                + (1 - propensity_scores) * self.cate_treated_models[ind].predict(X).reshape(m, -1)
+
+            if hasattr(self.cate_controls_models[ind], 'predict_proba'):
+                tau_hat = propensity_scores * self.cate_controls_models[ind].predict_proba(X)[:, 1].reshape(m, -1) \
+                    + (1 - propensity_scores) * self.cate_treated_models[ind].predict_proba(X)[:, 1].reshape(m, -1)
+            else:
+                tau_hat = propensity_scores * self.cate_controls_models[ind].predict(X).reshape(m, -1) \
+                    + (1 - propensity_scores) * self.cate_treated_models[ind].predict(X).reshape(m, -1)
+
             taus.append(tau_hat)
         taus = np.column_stack(taus).reshape((-1,) + self._d_t + self._d_y)  # shape as of m*d_t*d_y
         if self._d_y:
@@ -549,7 +564,10 @@ def const_marginal_effect(self, X):
         X = check_array(X)
         taus = []
         for model in self.final_models:
-            taus.append(model.predict(X))
+            if hasattr(model, 'predict_proba'):
+                taus.append(model.predict_proba(X)[:, 1])
+            else:
+                taus.append(model.predict(X))
         taus = np.column_stack(taus).reshape((-1,) + self._d_t + self._d_y)  # shape as of m*d_t*d_y
         if self._d_y:
             taus = transpose(taus, (0, 2, 1))  # shape as of m*d_y*d_t