Randomization Inference for Peer Effects

• Published in: Journal of the American Statistical Association Vol. 114; no. 528; pp. 1651 - 1664
• Main Authors: Li, Xinran, Ding, Peng, Lin, Qian, Yang, Dawei, Liu, Jun S.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.10.2019; Taylor & Francis Group, LLC; Taylor & Francis Ltd
• Subjects: academic achievement; Americans; Causal inference; College students; Colleges & universities; Design-based inference; Grade point average (GPA); hosts; Inference; Interference; issues and policy; Optimal treatment assignment; peers; Performance enhancement; Policy making; Randomization; Regression analysis; Spillover effect; Statistical methods; Statistics; Students; Theory and Methods; universities
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2018.1512863

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...