Doubly Robust Estimation in Missing Data and Causal Inference Models

TLDR

DR estimators remain consistent when either the missingness mechanism or the complete‑data model is correctly specified, offering analysts two chances to obtain valid inference in both missing‑data and causal‑inference settings. The article develops doubly robust estimators for ignorable missing‑data and causal‑inference models. The proposed method is applied to a cardiovascular clinical trial. Simulation studies show that the finite‑sample performance of the DR estimators aligns with theoretical predictions.

Abstract

The goal of this article is to construct doubly robust (DR) estimators in ignorable missing data and causal inference models. In a missing data model, an estimator is DR if it remains consistent when either (but not necessarily both) a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified. Because with observational data one can never be sure that either a missingness model or a complete data model is correct, perhaps the best that can be hoped for is to find a DR estimator. DR estimators, in contrast to standard likelihood-based or (nonaugmented) inverse probability-weighted estimators, give the analyst two chances, instead of only one, to make a valid inference. In a causal inference model, an estimator is DR if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified. Because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct, inference based on DR estimators should improve upon previous approaches. Indeed, we present the results of simulation studies which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict. The proposed method is applied to a cardiovascular clinical trial.