{"ID":2897863,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04332","arxiv_id":"2507.04332","title":"Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.04332","url_pdf":"https://arxiv.org/pdf/2507.04332v1","authors":"[\"Yi-Fu Fu\",\"Keng-Te Liao\",\"Shou-De Lin\"]","published":"2025-07-06T10:36:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}