报告题目：
Active Learning in Online Experiments with Interference
个人简介：
Chen Nan is currently an Associate Professor with the Department of Industrial Systems Engineering and Management at National University of Singapore. He received his Bachelor degree in Automation from Tsinghua University, and PhD degree in Industrial Engineering from University of Wisconsin Madison. His research focuses on modeling, monitoring and control of industrial processes, condition monitoring, and degradation modeling. He currently serves as department editor of IISE transactions. He is a member of INFORMS and IEEE.
讲座内容：
Causal inference has been applied in wide range of applications. Frequently, it assumes that there is no interference among subjects receiving the treatments. However, in real applications, interference is common. Ignoring it can lead to inaccurate interpretation of the results, and ineffective decision making. Our research studied a type of online experiments and tried to address interference by focusing on the estimation of
direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one’s outcomes, and (2) non-random treatment assignments influenced by confounders. Considering the fast execution of online experiments, we utilize the active learning to sequentially "design" the experiments to improve the efficiency. Through simulation data and online game applications, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements.
报告时间：2024年11月9日(周六)15:00-16:00
报告地点：必发9988集团 B309
邀请人： 徐照光 副教授
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