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A General Method for Detecting Interference Between Units in Randomized Experiments

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  • Peter M. Aronow

Abstract

Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, few options are available for testing this assumption. This article presents an ex post method for detecting interference between units in randomized experiments. With a test statistic of the analyst’s choice, a conditional randomization test allows for the calculation of the exact significance level of the causal dependence of outcomes on the treatment status of other units. The robustness of the method is demonstrated through simulation studies. Moreover, using this method, interference between units is detected in a field experiment designed to assess the effect of mailings on voter turnout in a U.S. primary election.

Suggested Citation

  • Peter M. Aronow, 2012. "A General Method for Detecting Interference Between Units in Randomized Experiments," Sociological Methods & Research, , vol. 41(1), pages 3-16, February.
    • Handle: RePEc:sae:somere:v:41:y:2012:i:1:p:3-16
    DOI: 10.1177/0049124112437535
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    References listed on IDEAS

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    1. Gerber, Alan S. & Green, Donald P. & Larimer, Christopher W., 2008. "Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment," American Political Science Review, Cambridge University Press, vol. 102(1), pages 33-48, February.
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    Cited by:

    1. Sarah Baird & Aislinn Bohren & Berk Ozler & Craig McIntosh, 2014. "Designing Experiments to Measure Spillover Effects," Working Papers 2014-11, The George Washington University, Institute for International Economic Policy.
    2. David Choi, 2017. "Estimation of Monotone Treatment Effects in Network Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1147-1155, July.
    3. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
    4. Taylor, Marshall A., 2019. "Visualization Strategies for Regression Estimates with Randomization Inference," SocArXiv bsd7g, Center for Open Science.
    5. Susan Athey & Dean Eckles & Guido W. Imbens, 2018. "Exact p-Values for Network Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 230-240, January.
    6. 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.
    7. Karlsson, Maria & Lundin, Mathias, 2016. "On statistical methods for labor market evaluation under interference between units," Working Paper Series 2016:24, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    8. Baylis, Kathy & Ham, Andres, 2015. "How important is spatial correlation in randomized controlled trials?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205586, Agricultural and Applied Economics Association.
    9. Sarah Baird & Aislinn Bohren & Craig McIntosh & Berk Ozler, 2015. "Designing Experiments to Measure Spillover Effects, Second Version," PIER Working Paper Archive 15-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jun 2015.
    10. Mathias Lundin & Maria Karlsson, 2014. "Estimation of causal effects in observational studies with interference between units," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 417-433, August.

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