{"ID":2859008,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07501","arxiv_id":"2510.07501","title":"Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death","abstract":"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.","short_abstract":"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, m...","url_abs":"https://arxiv.org/abs/2510.07501","url_pdf":"https://arxiv.org/pdf/2510.07501v1","authors":"[\"Sihyung Park\",\"Wenbin Lu\",\"Shu Yang\"]","published":"2025-10-08T19:54:43Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"stat.ME\"]","methods":"[]","has_code":false}