There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains `unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.

翻译：目前存在多种双重稳健估计方法；然而，应用文献中出现了一种令人担忧的趋势，即认为倾向性评分与调整后结果模型的组合会自动产生双重稳健估计量，和/或误用更复杂的现有双重稳健估计量。通过逆概率治疗（倾向性评分）加权最大似然估计拟合后经标准化（g公式）计算平均因果效应的标准连接函数广义线性模型（GLM），是一种双重稳健估计方法。我们的目标不仅是让读者能够使用这种我们称之为IPTW GLM的方法进行双重稳健估计，更是要充分理解其具备双重稳健性质的原因。为此，我们从多个角度清晰定义了理解该方法及其双重稳健性所需的所有概念。同时，我们强调：简单地将倾向性评分加权与调整后结果模型相结合通常不会产生双重稳健估计量。最后，我们希望能澄清一种误解：即使在使用双重稳健估计量时，人们仍可通过在结果模型中调整“不平衡”的变量来校正倾向性评分加权后残留的混杂。我们提供了模拟研究的R代码和真实开源数据示例，可逐步跟随操作以使用并理解IPTW GLM方法。此外，我们还与另一种更广为人知但同样简洁的双重稳健估计量进行了比较。