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Doubly Robust Estimation of Causal Effects.
- Source :
-
American Journal of Epidemiology . Apr2011, Vol. 173 Issue 7, p761-767. 7p. - Publication Year :
- 2011
-
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/. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00029262
- Volume :
- 173
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- American Journal of Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 59839051
- Full Text :
- https://doi.org/10.1093/aje/kwq439