Balancing utility and cost in dynamic treatment regimes
Holder： Yuqian Zhang（Renmin University of China ）
Time：2025-02-20 15:10-17:00
Location：Room 217, Guanghua Building 2
Abstract:
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.
About the Speaker:
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。
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