报告时间：2023年8月2日（星期三）10:00-11:00
报告地点：翡翠科教楼A座1603
报 告 人：王璐 教授
工作单位：密西根大学生物统计学系
举办单位：计算机与信息学院
报告简介：
In this talk, we present recent advances and statistical causal learning developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. We will first present a tree-based doubly robust reinforcement learning (T-RL) method, which builds a decision tree that maintains the nature of batch-mode reinforcement learning, and then a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs, which contributes to the existing literature in its non-greedy policy search and demonstrates outstanding performances even with a large number of covariates. In addition, we consider a common challenge with practical “restrictions” and develop a Restricted Tree-based Reinforcement Learning (RT-RL) method to address this challenge. We illustrate the method using an observational dataset to estimate a two-stage stepped-up DTR for guiding the level of care placement for adolescents with substance use disorder.
报告人简介：
王璐，博士，现任美国密西根大学生物统计学系终身教授，系副主任。2002年本科毕业于北京大学，2008年博士毕业于哈佛大学。研究领域包括评估优化动态治疗方案的统计方法、个性化医疗、因果推断、非参数和半参数回归、缺失数据分析、以及纵向（相关/聚类）数据分析等。在JASA、Biometrika、Biometrics、AoAS等学术期刊上发表论文139余篇，并合著了一章书籍。现任JASA和Biometrics的副主编。