Optimal Design of Experiments in the Presence of Interference

• 成果类型: Article
• 署名作者: Baird, Sarah; Bohren, J. Aislinn; McIntosh, Craig; Ozler, Berk
• 署名单位: George Washington University; University of Pennsylvania; University of California System; University of California San Diego; The World Bank
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00716
• 发表日期: 2018-12
• 页码: 844-860
• 关键词: Causal Inference; identification; education; impacts; cash

摘要

We formalize the optimal design of experiments when there is interference between units, that is, 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.