Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

TL;DR

This paper clarifies the properties of inverse probability of treatment weighting with generalized linear models (IPTW GLM) for doubly robust causal effect estimation, emphasizing correct application and dispelling common misconceptions.

Contribution

It provides a clear explanation of why IPTW GLM is doubly robust, distinguishes it from other methods, and offers practical R code for implementation and understanding.

Findings

IPTW GLM is a valid doubly robust estimator.

Misconceptions about combining propensity scores and outcome models are addressed.

The paper includes step-by-step R code for simulations and real data examples.

Abstract

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.