Citation
Keil, Alexander P.; Mooney, Stephen J.; Jonsson Funk, Michele L.; Cole, Stephen R.; Edwards, Jesse K.; & Westreich, Daniel (2018). Resolving an Apparent Paradox in Doubly Robust Estimators. American Journal of Epidemiology, 187(4), 891-892. PMCID: PMC5889031Abstract
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 outcomeY using models for both X andY; 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.”URL
http://dx.doi.org/10.1093/aje/kwx385Reference Type
Journal ArticleYear Published
2018Journal Title
American Journal of EpidemiologyAuthor(s)
Keil, Alexander P.Mooney, Stephen J.
Jonsson Funk, Michele L.
Cole, Stephen R.
Edwards, Jesse K.
Westreich, Daniel