Doubly Robust Estimation in Missing Data and Causal Inference Models

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 distribut...

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Bibliographic Details
Published in:Biometrics 2005-12, Vol.61 (4), p.962-973
Main Authors: Bang, Heejung, Robins, James M.
Format: Article
Language:English
Subjects:
Antidepressive Agents - therapeutic use
biometry
Causal inference
clinical trials
Cognitive Therapy - standards
Computer Simulation
Data Interpretation, Statistical
Depression - complications
Depression - drug therapy
Doubly robust estimation
Estimating techniques
Humans
Longitudinal data
Longitudinal Studies
Marginal structural model
Medical research
Missing data
Models, Statistical
Multicenter Studies as Topic
Myocardial Infarction - complications
Semiparametrics
Statistical methods
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Summary: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.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.1541-0420.2005.00377.x