提出M-learner框架，识别中介模型中治疗效应的异质性子群体。
M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
- 基于个体条件间接/总效应构建距离矩阵，用tSNE与K-means聚类发现子群。
- 在真实数据集上验证方法稳健有效，能精准识别不同响应模式的群体。
- 适用于医学、社会科学等需分析中介机制异质性的研究场景。
我们提出一种新方法——M-learner，用于估计中介模型中的异质性间接效应和总效应，并识别相关子群体。该方法包含四个关键步骤：首先计算个体层面的条件平均间接/总治疗效应；其次基于成对差异构建距离矩阵；然后使用tSNE将矩阵投影至低维欧氏空间，并通过K-means聚类识别子群结构；最后利用阈值法校准并优化聚类配置。据我们所知，这是首个专为中介情境下治疗效应异质性建模设计的方法。实验结果验证了该框架的鲁棒性与有效性。在真实世界Jobs II数据集上的应用展示了其广泛的适应性与潜在应用价值。代码已公开于https://anonymous.4open.science/r/M-learner-C4BB。
原文摘要 · Abstract (English)
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.
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