Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation

• URL: http://arxiv.org/abs/2507.04332v1
• Date: Sun, 06 Jul 2025 10:36:39 GMT
• Title: Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation
• Authors: Yi-Fu Fu, Keng-Te Liao, Shou-De Lin,
• Abstract summary: We highlight a common issue where many algorithms exhibit inconsistent learning behavior for the same instance across different group assignments.<n>We present a theoretical analysis showing that this inconsistency indeed contributes to higher test errors and cannot be resolved through conventional machine learning techniques.<n>We propose a general method called textbfConsistent Labeling Across Group Assignments (CLAGA) which eliminates the inconsistency and is applicable to any existing CATE estimation algorithm.
• Score: 4.938762852799707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 conventional machine learning techniques. To address this problem, we propose a general method called \textbf{Consistent Labeling Across Group Assignments} (CLAGA), which eliminates the inconsistency and is applicable to any existing CATE estimation algorithm. Experiments on both synthetic and real-world datasets demonstrate significant performance improvements with CLAGA.

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