Title: M-learner: A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: causal inference, causal decision making, mediation analysis, randomized clinical trial

Abstract: We propose a novel method, termed the M-learner, for estimating heterogeneous
indirect effects and identifying relevant subgroups within a mediation framework.
The procedure comprises four key steps. First, we compute individual-level con-
ditional average indirect treatment effect. Second, we construct a distance ma-
trix based on pairwise differences. Third, we apply t-SNE to project this ma-
trix into a low-dimensional Euclidean space, followed by K-means clustering
to identify subgroup structures. Finally, we calibrate and refine the clusters us-
ing a threshold-based procedure to determine the optimal configuration. This
is the first method capable of revealing the complex relationships among X, M,
and Y within mediation analysis while effectively controlling the Type I error
rate. Experimental results validate the robustness and effectiveness of the pro-
posed 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.

TL;DR: We propose a novel method, termed the M-learner, for estimating heterogeneous indirect treatment effects and identifying relevant subgroups within a mediation framework.