学术报告：Balancing utility and cost in dynamic treatment regimes

报告时间：3月26日（星期三）上午10:00-11:00

报告地点：沙河校区，二教107

主持人：潘蕊 教授

报告人：张宇谦，中国人民大学统计与大数据研究院，助理教授

报告摘要：Dynamic treatment regimes (DTRs) are personalized, adaptive strategies designed to guide the sequential allocation of treatments based on individual characteristics over time. Before each treatment assignment, covariate information is collected to refine treatment decisions and enhance their effectiveness. The more information we gather, the more precise our decisions can be. However, this also leads to higher costs during the data collection phase. In this work, we propose a balanced Q-learning method that strikes a balance between the utility of the DTRs and the costs associated with both treatment assignment and covariate assessment. The performance of the proposed method is demonstrated through extensive numerical studies.

报告人简介：张宇谦，中国人民大学统计与大数据研究院助理教授，博士生导师。2016年本科毕业于武汉大学，2022年博士毕业于美国加州大学圣地亚哥分校。主要研究方向包括因果推断、半监督学习、高维统计、机器学习理论、缺失数据、精准医疗等。文章发表于Annals of Statistics、Biometrika、Information and Inference等期刊。主持国家自然科学基金青年基金项目一项，参与面上项目一项。曾获美国统计协会非参数统计组最佳学生论文奖。

撰稿人：刘洁

审稿人：邓露