3 papers
stat.ME2026
Learning Robust Treatment Rules for Censored Data
Yifan Cui, Junyi Liu, Tao Shen +2
There is a fast-growing literature on estimating optimal treatment rules directly by maximizing the expected outcome. In biomedical studies and operations applications, censored su…
stat.ML2025
Quantile-Optimal Policy Learning under Unmeasured Confounding
Zhongren Chen, Siyu Chen, Zhengling Qi +2
We study quantile-optimal policy learning where the goal is to find a policy whose reward distribution has the largest -quantile for some . We focus on the offlin…
stat.ML2025
Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
Yuhan Li, Eugene Han, Yifan Hu +4
This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing r…