From d4a19a2a9728f13cd463df97a722affbf5734f7e Mon Sep 17 00:00:00 2001
From: Ian Delbridge <67915427+IanDelbridge@users.noreply.github.com>
Date: Fri, 12 Apr 2024 14:18:21 -0400
Subject: [PATCH] 20240207 honest leaf size (#753)

* refactor to just use fillTree for honestApproach
---
 causalml/inference/tree/uplift.pyx | 31 +-----------------------------
 1 file changed, 1 insertion(+), 30 deletions(-)

diff --git a/causalml/inference/tree/uplift.pyx b/causalml/inference/tree/uplift.pyx
index dfcbd263..1f60b908 100644
--- a/causalml/inference/tree/uplift.pyx
+++ b/causalml/inference/tree/uplift.pyx
@@ -564,36 +564,7 @@ class UpliftTreeClassifier:
             An array containing the outcome of interest for each unit.
         """
 
-        self.modifyEstimation(X_est, T_est, Y_est, self.fitted_uplift_tree)
-
-    def modifyEstimation(self, X_est, t_est, y_est, tree):
-        """ Modifies the leafs of the current decision tree to only contain unbiased estimates.
-        Applies the honest approach based on "Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects."
-        Args
-        ----
-        X_est : ndarray, shape = [num_samples, num_features]
-           An ndarray of the covariates used to calculate the unbiased estimates in the leafs of the decision tree.
-        T_est : array-like, shape = [num_samples]
-           An array containing the treatment group for each unit.
-        Y_est : array-like, shape = [num_samples]
-           An array containing the outcome of interest for each unit.
-        tree : object
-            object of DecisionTree class - the current decision tree that shall be modified
-        """
-
-        # Divide sets for child nodes
-        if tree.trueBranch or tree.falseBranch:
-            X_l, X_r, w_l, w_r, y_l, y_r = self.divideSet(X_est, t_est, y_est, tree.col, tree.value)
-
-            # recursive call for each branch
-            if tree.trueBranch is not None:
-                self.modifyEstimation(X_l, w_l, y_l, tree.trueBranch)
-            if tree.falseBranch is not None:
-                self.modifyEstimation(X_r, w_r, y_r, tree.falseBranch)
-
-        # classProb
-        if tree.results is not None:
-            tree.results = self.uplift_classification_results(t_est, y_est)
+        self.fillTree(X_est, T_est, Y_est, self.fitted_uplift_tree)
 
     def pruneTree(self, X, treatment_idx, y, tree, rule='maxAbsDiff', minGain=0.,
                   n_reg=0,