题目：Randomization Inference for Peer Effects
主讲：丁鹏
时间：2017年12月6日 14:30-15:30
地点：明德主楼 1030会议室
简介：
Peng Ding studied at Peking University from 2004-2011, obtaining B.S. in Mathematics, B.A. in Economics and M.S. in Statistics. He graduated with Ph.D. in Statistics from Harvard University, and joined the Harvard T. H. Chan School of Public Health as a postdoctoral researcher in Epidemiology until December 2015. From January 2016, he has been an Assistant Professor in Statistics at the University of California, Berkeley.
摘要：
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.