A Causal Bootstrap

Guido Imbens and Konrad Menzel

No 24833, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C01 C31 (search for similar items in EconPapers)
Date: 2018-07
New Economics Papers: this item is included in nep-ecm and nep-ore
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