Randomization Inference for Peer Effects

Xinran Li, Peng Ding, Qian Lin, Dawei Yang, Jun S. Liu

Research output: Contribution to journalArticlepeer-review

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, a top Chinese 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 policy makers of the university. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1651-1664
Number of pages14
JournalJournal of the American Statistical Association
Volume114
Issue number528
DOIs
StateAccepted/In press - 2019
Externally publishedYes

Keywords

  • Causal inference
  • Design-based inference
  • Grade point average (GPA)
  • Interference
  • Optimal treatment assignment
  • Spillover effect

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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