Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Am J Epidemiol. 2011 Apr 1;173(7):761-7. doi: 10.1093/aje/kwq439. Epub 2011 Mar 8.
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/.
双重稳健估计结合了一种结果回归形式和一种暴露（即倾向评分）模型，以估计暴露对结果的因果效应。当单独用于估计因果效应时，只有在统计模型正确指定的情况下，结果回归和倾向评分方法才是无偏的。双重稳健估计器将这两种方法结合在一起，使得只有 2 种模型中的 1 种需要正确指定，才能获得无偏的效应估计器。在这篇关于双重稳健估计器的介绍性文章中，作者提出了双重稳健估计的概念概述、一个简单的实例、模拟研究结果，该研究检查了估计和自举标准误差的性能，以及对这种方法的潜在优点和局限性的讨论。本文的补充材料（发布在杂志的网站上：http://aje.oupjournals.org/）包括对双重稳健性的演示（Web 附录 1）和一个用于双重稳健估计的 SAS 宏（SAS 研究所，北卡罗来纳州卡里）的描述，可在 http://www.unc.edu/~mfunk/dr/ 下载。
