A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity
Estimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average treatment effect (CATE) within the general frame...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/11/1739 |
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| author | Zhihao Zhao Congyang Zhou |
| author_facet | Zhihao Zhao Congyang Zhou |
| author_sort | Zhihao Zhao |
| collection | DOAJ |
| description | Estimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average treatment effect (CATE) within the general framework of meta-algorithms. RXlearner enhances the weighting mechanism of the traditional Xlearner to improve estimation accuracy. We establish non-asymptotic error bounds for RXlearner under a continuity classification criterion, specifically assuming that the response function satisfies Hölder continuity. Moreover, we show that these bounds are achievable by selecting an appropriate base learner. The effectiveness of the proposed method is validated through extensive simulation studies and a real-world data experiment. |
| format | Article |
| id | doaj-art-a7bbbe86d2964c749bbb3bd29e3481d4 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-a7bbbe86d2964c749bbb3bd29e3481d42025-08-20T03:46:46ZengMDPI AGMathematics2227-73902025-05-011311173910.3390/math13111739A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder ContinuityZhihao Zhao0Congyang Zhou1School of Statistics, Capital University of Economics and Business, Beijing 100070, ChinaSchool of Statistics, Capital University of Economics and Business, Beijing 100070, ChinaEstimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average treatment effect (CATE) within the general framework of meta-algorithms. RXlearner enhances the weighting mechanism of the traditional Xlearner to improve estimation accuracy. We establish non-asymptotic error bounds for RXlearner under a continuity classification criterion, specifically assuming that the response function satisfies Hölder continuity. Moreover, we show that these bounds are achievable by selecting an appropriate base learner. The effectiveness of the proposed method is validated through extensive simulation studies and a real-world data experiment.https://www.mdpi.com/2227-7390/13/11/1739conditional average treatment effectheterogeneous treatment effectcausal inferenceminimax optimalityHölder continuous |
| spellingShingle | Zhihao Zhao Congyang Zhou A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity Mathematics conditional average treatment effect heterogeneous treatment effect causal inference minimax optimality Hölder continuous |
| title | A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity |
| title_full | A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity |
| title_fullStr | A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity |
| title_full_unstemmed | A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity |
| title_short | A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity |
| title_sort | meta learning approach for estimating heterogeneous treatment effects under holder continuity |
| topic | conditional average treatment effect heterogeneous treatment effect causal inference minimax optimality Hölder continuous |
| url | https://www.mdpi.com/2227-7390/13/11/1739 |
| work_keys_str_mv | AT zhihaozhao ametalearningapproachforestimatingheterogeneoustreatmenteffectsunderholdercontinuity AT congyangzhou ametalearningapproachforestimatingheterogeneoustreatmenteffectsunderholdercontinuity AT zhihaozhao metalearningapproachforestimatingheterogeneoustreatmenteffectsunderholdercontinuity AT congyangzhou metalearningapproachforestimatingheterogeneoustreatmenteffectsunderholdercontinuity |