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A Causal Bootstrap

Author

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  • Guido Imbens
  • Konrad Menzel

Abstract

The bootstrap, introduced by Efron (1982), 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 that accounts for this uncertainty, and compare its properties to that of the classical bootstrap.

Suggested Citation

  • Guido Imbens & Konrad Menzel, 2018. "A Causal Bootstrap," NBER Working Papers 24833, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24833
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    Cited by:

    1. James J. Heckman & Ganesh Karapakula, 2019. "The Perry Preschoolers at Late Midlife: A Study in Design-Specific Inference," Working Papers 2019-034, Human Capital and Economic Opportunity Working Group.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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