Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation
跨组分配的一致标记:降低条件平均治疗效应估计方差
Numerous algorithms have been developed for Conditional Average Treatment Effect (CATE) estimation. In this paper, we first highlight a common issue where many algorithms exhibit inconsistent learning behavior for the same instance across different group assignments. We introduce a metric to quantify and visualize this inconsistency. Next, we present a theoretical analysis showing that this inconsistency indeed contributes to higher test errors and cannot be resolved through…