Munich School of Management and Munich Center of Health Sciences, Ludwig-Maximilians-Universität Munich, Munich, Germany.
Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany.
Med Decis Making. 2022 Feb;42(2):156-167. doi: 10.1177/0272989X211027181. Epub 2021 Jul 6.
This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a "doubly robust" method in that it requires only either the propensity or outcome model to be correctly specified but not both. Even though this estimator has been known for years, it is rarely used in practice. After explaining the estimator and proving the double robustness property, I conduct a simulation study to compare the AIPW efficiency with IPW and regression under different scenarios of misspecification. In 2 real-world examples, I provide a step-by-step guide on implementing the AIPW estimator in practice. I show that it is an easily usable method that extends the IPW to reduce variability and improve estimation accuracy.[Box: see text].
本文讨论了增强逆倾向评分加权（AIPW）估计量作为平均处理效应的估计量。AIPW 结合了基于回归的估计量和逆概率加权（IPW）估计量的特性，因此是一种“双重稳健”的方法，因为它只需要倾向或结果模型中的一个正确指定，而不是两个都要正确指定。尽管这种估计器已经存在多年，但在实践中很少使用。在解释了估计器并证明了双重稳健性之后，我进行了一项模拟研究，比较了在不同的误设定情况下，AIPW 效率与 IPW 和回归的比较。在 2 个实际例子中，我提供了一个在实践中实现 AIPW 估计器的分步指南。我表明，它是一种易于使用的方法，可以扩展 IPW 以减少变异性并提高估计准确性。[框：见文本]。
