Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

Park, Sihyung, Lu, Wenbin, Yang, Shu

arXiv.org Machine Learning 

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.

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