Randomization Inference for Peer Effects

• Activity Number: 473 - Tools of Inferential Decision Making in Education and Behavioral Sciences
• Type: Contributed
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Social Statistics Section
• Author(s): Xinran Li* and Peng Ding and Qian Lin and Dawei Yang and Jun Liu
• Companies: Harvard University and University of California, Berkeley and Harvard University and Peking University and Harvard University
• Keywords: Causal inference ; Design-based inference ; Grade point average ; Interference ; Optimal treatment assignment ; Spillover effect

Abstract

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, or recommendation. 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 peer types.