Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

Sihyung Park; Wenbin Lu; Shu Yang

NeurIPS 2025poster0 citations

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.

Dynamic Treatment RegimesPanel DataPrincipal StratificationTruncation by DeathMultiple RobustnessNonparametric EfficiencyPersonalized Medicine

BibTeX

@inproceedings{
park2025evaluating,
title={Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death},
author={Sihyung Park and Wenbin Lu and Shu Yang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=feLdTALuq3}
}