Doubly robust estimation of causal effects.

Published

Journal Article

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/.

Full Text

Duke Authors

Cited Authors

  • Funk, MJ; Westreich, D; Wiesen, C; Stürmer, T; Brookhart, MA; Davidian, M

Published Date

  • April 1, 2011

Published In

Volume / Issue

  • 173 / 7

Start / End Page

  • 761 - 767

PubMed ID

  • 21385832

Pubmed Central ID

  • 21385832

Electronic International Standard Serial Number (EISSN)

  • 1476-6256

Digital Object Identifier (DOI)

  • 10.1093/aje/kwq439

Language

  • eng

Conference Location

  • United States