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Optimal Design of Experiments in the Presence of Interference

Sarah Baird1; J. Aislinn Bohren2; Craig McIntosh3; Berk Özler4

1 George Washington University · 2 University of Pennsylvania · 3 University of California San Diego · 4 World Bank

The Review of Economics and Statistics 2018 open access

We formalize the optimal design of experiments when there is interference between units, i.e. an individual's outcome depends on the outcomes of others in her group. We focus on randomized saturation designs, two-stage experiments that first randomize treatment saturation of a group, then individual treatment assignment. We map the potential outcomes framework with partial interference to a regression model with clustered errors, calculate standard errors of randomized saturation designs, and derive analytical insights about the optimal design. We show that the power to detect average treatment effects declines precisely with the ability to identify novel treatment and spillover effects.

DOI
10.1162/rest_a_00716
Volume
100 (5)
Pages
844-860
Language
en
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