Resolving an apparent paradox in doubly robust estimators

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Analytics

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Creator
  • Keil, A.P.
    • Gillings School of Global Public Health, Department of Epidemiology
  • Mooney, S.J.
    • Other Affiliation: University of Washington
  • Jonsson Funk, M.
    • Gillings School of Global Public Health, Department of Epidemiology
  • Cole, S.R.
    • Gillings School of Global Public Health, Department of Epidemiology
  • Edwards, J.K.
    • Gillings School of Global Public Health, Department of Epidemiology
  • Westreich, D.
    • Gillings School of Global Public Health, Department of Epidemiology
Abstract
  • Doubly robust estimators are an approach used for estimating causal effects, usually based on fitting 2 statistical models (1). As the initial motivating example, Scharfstein et al. defined a robust estimator of the causal effect of some exposure X on outcome Y using models for both X and Y⁠; they demonstrated that such an estimator is consistent if “at least one of the [fitted] models… is correct” (2, p. 1142). Such estimators were later termed “doubly robust” (3, p. 6). Here, we demonstrate that using common (but incorrect) intuition about what makes a model “correct” or “incorrect” can turn doubly robust estimators into estimators that are inconsistent if at least one of the fitted models is wrong. We introduce and resolve this double-robust paradox, demonstrating what must be meant by “correct model.”
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DOI
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Resource type
  • Article
Rights statement
  • In Copyright
Journal title
  • American Journal of Epidemiology
Journal volume
  • 187
Journal issue
  • 4
Page start
  • 891
Page end
  • 892
Language
  • English
ISSN
  • 1476-6256

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