Speaker：Ruoqing Zhu (University of Illinois Urbana-Champaign)

Time：2024-5-30, 15:00

Location：Conference Room 686 at the 6th floor of Shuli Building at Haiyun Campus

Abstract:

Reinforcement learning has become an essential and powerful tool for modeling sequential decision data and inferring the optimal decision rule. Although enjoying enormous success in various fields, it still faces critical challenges in applications in medicine and human behavior studies, where the sample size can be small, noise is large, and unobserved confounders could be present. In this talk, we introduce two recent works that separately address two issues. One is a regularized framework that leads to more conservative and potentially safer treatment rules. This method is applied to an insulin dose-finding problem for diabetic management. Another is proposed to address the unobserved confounding issue in a partially observed Markov decision process setting. We utilize the proximal causal framework to estimate the value function of any potential treatment strategy. This approach is applied to a family relationship study that aims to understand the strategies for improving romantic relationships.