Doubly Robust Estimation of Causal Effects

作者：Michele Jönsson Funk, Daniel J. Westreich, Chris Wiesen, Til Stürmer‎, M. Alan Brookhart, Marie Davidian · 发表于：American Journal of Epidemiology · 年份：2011 · DOI：10.1093/aje/kwq439 · 被引用次数：1190 · 研究领域：Advanced Causal Inference Techniques、Statistical Methods and Inference、Statistical Methods and Bayesian Inference

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/.