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Bounded, efficient and doubly robust estimation with inverse weighting

Tan, Z.·2010·Biometrika

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.

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Metadata

Authors
Tan, Z.
Venue
Biometrika
Published
2010-07-26
Volume
97
Issue
3
Pages
661-682
ISSN
  • 0006-3444 (ISSN-L)
  • 1464-3510
OpenAlex
W1999188374
Cited by
305
References
1
FWCI
10.77
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bounded, efficient and doubly robust estimation with inverse weighting. https://app.cochat.ai/papers/10.1093/biomet/asq035
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Tan, Z. (2010). Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika. https://doi.org/10.1093/biomet/asq035

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