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

Author

Listed:
  • Sarah Baird

    (Department of Global Health, George Washington University)

  • Aislinn Bohren

    (Department of Economics, University of Pennsylvania)

  • Craig McIntosh

    (Department of Economics, UC San Diego)

  • Berk Ozler

    (The World Bank)

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.

Suggested Citation

  • Sarah Baird & Aislinn Bohren & Craig McIntosh & Berk Ozler, 2017. "Optimal Design of Experiments in the Presence of Interference*, Second Version," PIER Working Paper Archive 16-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 30 Nov 2017.
    • Handle: RePEc:pen:papers:16-025
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    References listed on IDEAS

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    Cited by:

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    3. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Jan 2023.
    4. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.

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    More about this item

    Keywords

    Experimental Design; Causal Inference;

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • O22 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Project Analysis
    • I25 - Health, Education, and Welfare - - Education - - - Education and Economic Development

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