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Measuring the effect of peers on individuals' outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and unobservable ways. Group formation experiments avoid this problem by randomly assigning individuals to groups and observing their responses; for example, do first‐year students have better grades when they are randomly assigned roommates who have stronger academic backgrounds? In this paper, we propose randomization‐based permutation tests for group formation experiments, extending classical Fisher Randomization Tests to this setting. The proposed tests are justified by the randomization itself, require relatively few assumptions, and are exact in finite samples. This approach can also complement existing strategies, such as linear‐in‐means models, by using a regression coefficient as the test statistic. We apply the proposed tests to two recent group formation experiments.more » « less
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Ding, Yi; Toulis, Panos (, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)
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