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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.17917 |
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| _version_ | 1866913987268968448 |
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| author | Li, Xingyu Liu, Qing Jiang, Tony Xia, Hong Amy Hobbs, Brian P. Wei, Peng |
| author_facet | Li, Xingyu Liu, Qing Jiang, Tony Xia, Hong Amy Hobbs, Brian P. Wei, Peng |
| contents | We propose a novel method, termed the M-learner, for estimating heterogeneous indirect and total treatment effects and identifying relevant subgroups within a mediation framework. The procedure comprises four key steps. First, we compute individual-level conditional average indirect/total treatment effect Second, we construct a distance matrix based on pairwise differences. Third, we apply tSNE to project this matrix into a low-dimensional Euclidean space, followed by K-means clustering to identify subgroup structures. Finally, we calibrate and refine the clusters using a threshold-based procedure to determine the optimal configuration. To the best of our knowledge, this is the first approach specifically designed to capture treatment effect heterogeneity in the presence of mediation. Experimental results validate the robustness and effectiveness of the proposed framework. Application to the real-world Jobs II dataset highlights the broad adaptability and potential applicability of our method.Code is available at https: //anonymous.4open.science/r/M-learner-C4BB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model Li, Xingyu Liu, Qing Jiang, Tony Xia, Hong Amy Hobbs, Brian P. Wei, Peng Machine Learning Methodology We propose a novel method, termed the M-learner, for estimating heterogeneous indirect and total treatment effects and identifying relevant subgroups within a mediation framework. The procedure comprises four key steps. First, we compute individual-level conditional average indirect/total treatment effect Second, we construct a distance matrix based on pairwise differences. Third, we apply tSNE to project this matrix into a low-dimensional Euclidean space, followed by K-means clustering to identify subgroup structures. Finally, we calibrate and refine the clusters using a threshold-based procedure to determine the optimal configuration. To the best of our knowledge, this is the first approach specifically designed to capture treatment effect heterogeneity in the presence of mediation. Experimental results validate the robustness and effectiveness of the proposed framework. Application to the real-world Jobs II dataset highlights the broad adaptability and potential applicability of our method.Code is available at https: //anonymous.4open.science/r/M-learner-C4BB. |
| title | M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2505.17917 |