主 题：Randomization Inference for Peer Effects
主办单位：统计研究中心 统计学院 科研处
Peng Ding is Assistant Professor in the Department of Statistics, University of California, Berkeley. He obtained his Ph.D. from the Department of Statistics, Harvard University, and worked as a postdoctoral researcher at the Harvard School of Public Health. His research interests are causal inference and philosophy of science.
Many previous causal inference studies required no interference among units, that is, the potential outcomes of a unit do not depend on the treatments of other units. This no-interference assumption, however, becomes unreasonable when units are partitioned into groups and they interact with other units within groups. In a motivating education example from Peking University, students are admitted either through the college entrance exam (also known as Gaokao), or recommendation (often based on Olympiads in various subjects). Right after entering college, students are randomly assigned to different dorms, each of which hosts four students. Because students within the same dorm live together almost every day and they interact with each other intensively, it is very likely that peer effects exist and the no-interference assumption is violated. More importantly, understanding peer effects among students gives useful guidance for future roommate assignment to improve the overall performances of the students. Methodologically, we define peer effects in terms of potential outcomes, and propose a randomization-based inference framework to study peer effects in general settings with arbitrary numbers of peers and arbitrary numbers of student types. Our inferential procedure does not require any parametric modeling assumptions on the outcome distributions. Our analysis of the data set from Peking University gives useful practical guidance for policy makers.