A theory of experimenters: Robustness, randomization, and balance†

Abhijit V. Banerjee, Sylvain Chassang, Sergio Montero, Erik Snowberg

    Research output: Contribution to journalArticle

    Abstract

    This paper studies the problem of experiment design by an ambiguity-averse decision-maker who trades off subjective expected performance against robust performance guarantees. This framework accounts for real-world experimenters’ preference for randomization. It also clarifies the circumstances in which randomization is optimal: when the available sample size is large and robustness is an important concern. We apply our model to shed light on the practice of rerandomization, used to improve balance across treatment and control groups. We show that rerandomization creates a trade-off between subjective performance and robust performance guarantees. However, robust performance guarantees diminish very slowly with the number of rerandomizations. This suggests that moderate levels of rerandomization usefully expand the set of acceptable compromises between subjective performance and robustness. Targeting a fixed quantile of balance is safer than targeting an absolute balance objective.

    Original languageEnglish (US)
    Pages (from-to)1206-1230
    Number of pages25
    JournalAmerican Economic Review
    Volume110
    Issue number4
    DOIs
    StatePublished - Apr 2020

    ASJC Scopus subject areas

    • Economics and Econometrics

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    Banerjee, A. V., Chassang, S., Montero, S., & Snowberg, E. (2020). A theory of experimenters: Robustness, randomization, and balance†. American Economic Review, 110(4), 1206-1230. https://doi.org/10.1257/aer.20171634