第22期工作坊，主讲人为曹迥仪。
题目：Experimental Evaluation of Dynamic Individualized Treatment Rules
内容提要
In recent years, machine learning algorithms have been used to develop individualized treatment rules (ITRs). Applications of such methodology include personalized medicine and micro-targeting in business and politics. What is lacking in the literature, however, is a robust way to evaluate the empirical performance of ITRs before implementing them in practice. Recently, Imai and Li (2021) introduced an experimental evaluation methodology that only relies upon the randomization of treatment assignment and random sampling of units without making any modeling assumptions. Thus, the methodology is applicable to ITRs that are derived using any generic machine learning algorithm. We extend this methodology to the dynamic ITRs in sequential multiple assignment randomized trials (SMART). We introduce an evaluation metric that decomposes the performance of a dynamic ITR into separate time periods while accounting for a budget constraint. We propose an unbiased estimator of this evaluation metric and derive its finite-sample variances. We conduct simulation studies to show that the confidence intervals based on the proposed finite-sample variance estimator have a good coverage even in a small sample size. Finally, we apply our methodology to the experimental data from the Tennessee's Student Teacher Achievement Ratio (STAR) project.
主讲人简介
曹迥仪，清华大学政治学系博士生。威斯康星大学麦迪逊分校理学学士（2019）；芝加哥大学统计系硕士（2021）。主要研究兴趣包括政治学方法论，计算社会科学，因果推断与其在机器学习的应用。
其学术成果曾展示于
the Atlantic Casual Inference Conference
the Asian Political Methodology Society
the Annals of Applied Statistics
评议人
刘诗尧，美国麻省理工大学政治科学博士，研究方向为政治科学方法论和中国政治。现于纽约大学阿布扎比分校从事博士后工作，将于明年加入北京大学南南合作与发展学院担任助理教授。
工作坊信息
时 间：
2022/03/23 19:00-20:30 (GMT+08:00) 中国标准时间 - 北京
会议号：
腾讯会议：619-781-032
主持人：
政治学系博士生张竞衔
欢迎各位老师与同学积极参与！
