Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

Sihyung Park, Wenbin Lu, Shu Yang

Advances in Neural Information Processing Systems 38 (NeurIPS 2025) Main Conference Track

Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage DTRs, demonstrating its robustness and efficiency. Empirical validation and an application to electronic health records showcase its utility for personalized treatment optimization.