3 papers
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 offline…
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…
stat.ME2024
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…