Randomization Inference for Peer Effects
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...
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          | Published in | Journal of the American Statistical Association Vol. 114; no. 528; pp. 1651 - 1664 | 
|---|---|
| Main Authors | , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Alexandria
          Taylor & Francis
    
        02.10.2019
     Taylor & Francis Group, LLC Taylor & Francis Ltd  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0162-1459 1537-274X 1537-274X  | 
| DOI | 10.1080/01621459.2018.1512863 | 
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| 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. | 
    
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| AbstractList | 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. 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.  | 
    
| Author | Yang, Dawei Lin, Qian Li, Xinran Ding, Peng Liu, Jun S.  | 
    
| Author_xml | – sequence: 1 givenname: Xinran surname: Li fullname: Li, Xinran organization: Department of Statistics, Harvard University – sequence: 2 givenname: Peng surname: Ding fullname: Ding, Peng email: pengdingpku@berkeley.edu organization: Department of Statistics, University of California – sequence: 3 givenname: Qian surname: Lin fullname: Lin, Qian organization: Center for Statistical Science, Department of Industrial Engineering, Tsinghua University – sequence: 4 givenname: Dawei surname: Yang fullname: Yang, Dawei organization: Bureau of Personnel of Chinese Academy of Sciences & School of Education of Peking University, Beijing – sequence: 5 givenname: Jun S. surname: Liu fullname: Liu, Jun S. organization: Department of Statistics, Harvard University  | 
    
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| SubjectTerms | 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  | 
    
| Title | Randomization Inference for Peer Effects | 
    
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