Statistics Seminar（2018-20）

Topic: Randomization inference for peer effects, with application to the roommate assignment in Peking University

Speaker: Peng Ding (丁鹏), UC Berkeley

Time: Thursday, December 13, 14:00-15:00

Place: Room K02, Guanghua Building 2

Abstract:

Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, Peking University admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The university randomly assigns students to dorms, each of which hosts four students. Students within the same dorm live together and have extensive interactions. Therefore, it is likely that peer effects exist and the no-interference assumption does not hold. It is important to understand peer effects because they give useful guidance for future roommate assignment to improve the performance of students. We define peer effects using potential outcomes. We then propose a randomization-based inference framework to study peer effects with arbitrary numbers of peers and peer types. Our inferential procedure does not assume any parametric model on the outcome distribution. Our analysis gives useful practical guidance for policymakers of Peking University.

Introduction:

Peng Ding received Ph.D. from the Harvard Statistics Department in May 2015 and worked as a postdoctoral researcher in the Harvard Epidemiology Department until December 2015. Since January 2016, he has been Assistant Professor in the Statistics Department of University of California, Berkeley. His research interests include causal inference, missing data, and experimental design.

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