Optimal Design of Experiments in the Presence of Interference*, Second Version

Sarah Baird (), Aislinn Bohren, Craig McIntosh () and Berk Özler
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Sarah Baird: Department of Global Health, George Washington University
Craig McIntosh: Department of Economics, UC San Diego

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: This paper formalizes the optimal design of randomized controlled trials (RCTs) in the presence of interference between units, where an individual's outcome depends on the behavior and outcomes of others in her group. We focus on randomized saturation (RS) designs, which are two-stage RCTs that first randomize the treatment saturation of a group, then randomize individual treatment assignment. Our main contributions are to map the potential outcomes framework with partial interference to a regression model with clustered errors, calculate the statistical power of different RS designs, and derive analytical insights for how to optimally design an RS experiment. We show that the power to detect average treatment effects declines precisely with the ability to identify novel treatment and spillover estimands, such as how effects vary with the intensity of treatment. We provide software that assists researchers in designing RS experiments.

Keywords: Experimental Design; Causal Inference (search for similar items in EconPapers)
JEL-codes: C93 I25 O22 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2017-11-30, Revised 2017-11-30
New Economics Papers: this item is included in nep-ecm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:16-025

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