Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915373363757056 |
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| author | Fu, Yi-Fu Liao, Keng-Te Lin, Shou-De |
| author_facet | Fu, Yi-Fu Liao, Keng-Te Lin, Shou-De |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation Fu, Yi-Fu Liao, Keng-Te Lin, Shou-De Machine Learning Methodology 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. |
| title | Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2507.04332 |