Article,
Doubly robust estimation of causal effects.
American journal of epidemiology, 173 (7): 761-7 (April 2011)6075<br/>JID: 7910653; aheadofprint;<br/><br/>Causalitat; Propensity score; IPTW.
DOI: 10.1093/aje/kwq439
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/.
Tags
- causality
- computersimulation
- confidenceintervals
- confoundingfactors(epidemiology)
- epidemiologicmethods
- humans
- likelihoodfunctions
- models
- montecarlomethod
- propensityscore
- regressionanalysis
- statistical
