AI RESEARCH
Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data
arXiv CS.LG
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ArXi:2603.05568v1 Announce Type: cross Integrative analysis of multiple datasets for estimating optimal individualized treatment rules (ITRs) can enhance decision efficiency. A central challenge is posterior shift, wherein the conditional distribution of potential outcomes given covariates differs between source and target populations. We propose a prior information-based distributionally robust ITR (PDRO-ITR) that maximizes the worst-case policy value over a covariate-dependent distributional uncertainty set, ensuring robust performance under posterior shift.