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      Doubly robust estimation of causal effects.

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          Abstract

          Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/~mfunk/dr/.

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          Author and article information

          Journal
          Am J Epidemiol
          American journal of epidemiology
          Oxford University Press (OUP)
          1476-6256
          0002-9262
          Apr 01 2011
          : 173
          : 7
          Affiliations
          [1 ] Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. mfunk@unc.edu
          Article
          kwq439
          10.1093/aje/kwq439
          3070495
          21385832
          e8de86df-4b5b-45d3-857f-231fb7fc53f6
          History

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