Randomization Inference for Peer Effects

TL;DR

This paper develops a randomization inference framework to study peer effects in settings where units interact within groups, relaxing the no-interference assumption common in causal inference, with practical applications to university dorm assignments.

Contribution

It introduces a non-parametric, randomization-based method to estimate peer effects in complex group interactions, extending causal inference beyond traditional assumptions.

Findings

Provides a practical inference procedure for peer effects with arbitrary peer configurations.

Offers policy guidance for university dormitory assignments to enhance student performance.

Demonstrates the method's applicability in real-world educational settings.

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 university in China 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 in China.