Time: 16:00-17:00 on Tuesday, Apr.8, 2025

Location: Room 302, Starr Building

Host: Dr. Guorong DAI, Statistics and Data Science, Fudan SoM

Speaker: Assistant Professor Yuqian ZHANG, Renmin University of China

Topic: Balancing utility and cost in dynamic treatment regimes

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.

Bio: Yuqian ZHANG is an Assistant Professor and Doctoral Supervisor at the Institute of Statistics and Big Data, Renmin University of China. He graduated with a Bachelor’s degree from Wuhan University in 2016 and obtained his Ph.D. from the University of California, San Diego in 2022. His research interests cover causal inference, semi-supervised learning, high-dimensional statistics, machine learning theory, missing data, and precision medicine, among other fields. His articles have been published in journals such as the Annals of Statistics, Biometrika, and Information and Inference. He is currently the principal investigator of a Youth Fund project from the National Natural Science Foundation of China and has also participated in a General Program project. He was once awarded the Best Student Paper Prize by the Nonparametric Statistics Section of the American Statistical Association.