Doubly Robust Estimation of Causal Effects
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ID: 300135
2011
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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/.
| Reference Key |
openalex_W2144387798
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| Authors | Michele Jönsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, Marie Davidian |
| Journal | american journal of epidemiology |
| Year | 2011 |
| DOI |
10.1093/aje/kwq439
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| URL | |
| Keywords | Keywords not found |
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