報告題目：動態(tài)決策的因果推斷機(jī)器學(xué)習(xí)
報告人：王璐 教授
單位：密西根大學(xué)生物統(tǒng)計學(xué)系
報告時間：2023年8月2號（周三）上午10:00-11:00
報告地點：翡翠科教樓A座1602
報告摘要：
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.
個人簡介：王璐，博士，現(xiàn)任美國密西根大學(xué)生物統(tǒng)計學(xué)系終身教授，系副主任。2002年本科畢業(yè)于北京大學(xué)，2008年博士畢業(yè)于哈佛大學(xué)。研究領(lǐng)域包括評估優(yōu)化動態(tài)治療方案的統(tǒng)計方法、個性化醫(yī)療、因果推斷、非參數(shù)和半?yún)?shù)回歸、缺失數(shù)據(jù)分析、以及縱向（相關(guān)/聚類）數(shù)據(jù)分析等。在JASA、Biometrika、Biometrics、AoAS等學(xué)術(shù)期刊上發(fā)表論文139余篇，并合著了一章書籍?，F(xiàn)任JASA和Biometrics的副主編。
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