Bounded, efficient and doubly robust estimation with inverse weighting
Open Access
- 26 July 2010
- journal article
- research article
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 97 (3), 661-682
- https://doi.org/10.1093/biomet/asq035
Abstract
Consider estimating the mean of an outcome in the presence of missing data or estimating population average treatment effects in causal inference. A doubly robust estimator remains consistent if an outcome regression model or a propensity score model is correctly specified. We build on a previous nonparametric likelihood approach and propose new doubly robust estimators, which have desirable properties in efficiency if the propensity score model is correctly specified, and in boundedness even if the inverse probability weights are highly variable. We compare the new and existing estimators in a simulation study and find that the robustified likelihood estimators yield overall the smallest mean squared errors.Keywords
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