報告時間：2023年8月2日（星期三）10:00-11:00

報告地點：翡翠科教樓A座1603

報 告 人：王璐 教授

工作單位：密西根大學生物統計學系

舉辦單位：計算機與信息學院

報告簡介：

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.

報告人簡介：

王璐，博士，現任美國密西根大學生物統計學系終身教授，系副主任。2002年本科畢業于北京大學，2008年博士畢業于哈佛大學。研究領域包括評估優化動態治療方案的統計方法、個性化醫療、因果推斷、非參數和半參數回歸、缺失數據分析、以及縱向（相關/聚類）數據分析等。在JASA、Biometrika、Biometrics、AoAS等學術期刊上發表論文139余篇，并合著了一章書籍?，F任JASA和Biometrics的副主編。