Doubly Robust Estimation in Missing Data and Causal Inference Models

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ID: 291318
2005
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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.
Reference Key
openalex_W2137370054 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Heejung Bang, James M. Robins
Journal biometrics
Year 2005
DOI
10.1111/j.1541-0420.2005.00377.x
URL
https://doi.org/10.1111/j.1541-0420.2005.00377.x
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