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  • 16 - Spillover Effects in Experimental Data

  • from Part IV - Experimental Analys is and Presentation
  • Edited by James N. Druckman, Northwestern University, Illinois, Donald P. Green, Columbia University, New York
  • Book:
    Advances in Experimental Political Science
    Published online:
    08 March 2021
    Print publication:
    01 April 2021, pp 289-319

  • Summary

    We present current methods for estimating treatment effects and spillover effects under “interference”, a term which covers a broad class of situations in which a unit’s outcome depends not only on treatments received by that unit, but also on treatments received by other units. To the extent that units react to each other, interact, or otherwise transmit effects of treatments, valid inference requires that we account for such interference, which is a departure from the traditional assumption that units’ outcomes are affected only by their own treatment assignment. Interference and associated spillovers may be a nuisance or they may be of substantive interest to the researcher. In this chapter, we focus on interference in the context of randomized experiments. We review methods for when interference happens in a general network setting. We then consider the special case where interference is contained within a hierarchical structure. Finally, we discuss the relationship between interference and contagion. We use the interference R package and simulated data to illustrate key points. We consider efficient designs that allow for estimation of the treatment and spillover effects and discuss recent empirical studies that try to capture such effects.

  • A Note on Dropping Experimental Subjects who Fail a Manipulation Check

  • Peter M. Aronow, Jonathon Baron, Lauren Pinson
  • Journal:
    Political Analysis / Volume 27 / Issue 4 / October 2019
    Published online by Cambridge University Press:
    30 May 2019, pp. 572-589
    • Article
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  • Dropping subjects based on the results of a manipulation check following treatment assignment is common practice across the social sciences, presumably to restrict estimates to a subpopulation of subjects who understand the experimental prompt. We show that this practice can lead to serious bias and argue for a focus on what is revealed without discarding subjects. Generalizing results developed in Zhang and Rubin (2003) and Lee (2009) to the case of multiple treatments, we provide sharp bounds for potential outcomes among those who would pass a manipulation check regardless of treatment assignment. These bounds may have large or infinite width, implying that this inferential target is often out of reach. As an application, we replicate Press, Sagan, and Valentino (2013) with a design that does not drop subjects that failed the manipulation check and show that the findings are likely stronger than originally reported. We conclude with suggestions for practice, namely alterations to the experimental design.

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