Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data
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. The uncertainty set is constructed as an individualized combination of source distributions, with weights combining prior source-membership probabilities and deviation t...
| Authors | Wenhai Cui, Wen Su, Xingqiu Zhao |
|---|---|
| Primary category | stat.ML |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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