Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality
We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially treat individuals whose potential outcomes under treatment exceed a covariate-specific control reference, while limiting false discoveries among treated individuals. We propose Partial-Feedback Adaptive Individualized Treatment Selection (PAITS), which initializes learning from historical data and combines sequential estimation of local null risks, randomized exploration, and feedback-driven updates of the treatment selection threshold. Under regularity conditions, we establish asymptotic false discovery rate control and sublinear gap-weight...
| Authors | Runzhe Gu, Zinan Zhao, Wenguang Sun et al. |
|---|---|
| Primary category | stat.ML |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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