Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
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结合广义线性结果模型的逆概率处理加权双重稳健估计
- 研究问题
- 何种倾向得分加权与结果模型组合具有双重稳健性，为何仅同时使用两类模型并不能自动保证这一性质？
- 方法与思路
- 作者以逆概率处理加权极大似然拟合典则链接的广义线性模型，再用 g 公式标准化估计平均因果效应。
- 材料陈述的贡献
- 摘要解释 IPTW GLM 的双重稳健性质，澄清加权后调整不平衡变量的误解，并提供模拟、公开数据示例与 R 代码。
- 阅读关注点
- 应核对典则链接与估计方程的作用、双重稳健成立的具体条件，以及标准化步骤、方差估计和对照估计量。
- 阅读边界
- 所据为 arXiv 预印本摘要，同行评审与期刊接收未核实；摘要未列出完整因果识别条件，不能把双重稳健理解为消除所有混杂。
原文摘要
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.