A causal bootstrap

Guido Imbens, Konrad Menzel

    Research output: Contribution to journalArticlepeer-review


    The bootstrap, introduced by The Jackknife, the Bootstrap and Other Resampling Plans ((1982), SIAM), has become a very popular method for estimating variances and constructing confidence intervals. A key insight is that one can approximate the properties of estimators by using the empirical distribution function of the sample as an approximation for the true distribution function. This approach views the uncertainty in the estimator as coming exclusively from sampling uncertainty. We argue that for causal estimands the uncertainty arises entirely, or partially, from a different source, corresponding to the stochastic nature of the treatment received. We develop a bootstrap procedure for inference regarding the average treatment effect that accounts for this uncertainty, and compare its properties to that of the classical bootstrap. We consider completely randomized and observational designs as well as designs with imperfect compliance.

    Original languageEnglish (US)
    Pages (from-to)1460-1488
    Number of pages29
    JournalAnnals of Statistics
    Issue number3
    StatePublished - Jun 2021


    • Bootstrap
    • Causal inference
    • Partial identification

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty


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