Improved double-robust estimation in missing data and causal inference models
Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-ro...
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paper:paper_00063444_v99_n2_p439_Rotnitzky2023-06-08T14:31:12Z Improved double-robust estimation in missing data and causal inference models Sued, Mariela Drop-out Marginal structural model Missing at random Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory. © 2012 Biometrika Trust. Fil:Sued, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2012 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00063444_v99_n2_p439_Rotnitzky http://hdl.handle.net/20.500.12110/paper_00063444_v99_n2_p439_Rotnitzky |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Drop-out Marginal structural model Missing at random |
spellingShingle |
Drop-out Marginal structural model Missing at random Sued, Mariela Improved double-robust estimation in missing data and causal inference models |
topic_facet |
Drop-out Marginal structural model Missing at random |
description |
Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory. © 2012 Biometrika Trust. |
author |
Sued, Mariela |
author_facet |
Sued, Mariela |
author_sort |
Sued, Mariela |
title |
Improved double-robust estimation in missing data and causal inference models |
title_short |
Improved double-robust estimation in missing data and causal inference models |
title_full |
Improved double-robust estimation in missing data and causal inference models |
title_fullStr |
Improved double-robust estimation in missing data and causal inference models |
title_full_unstemmed |
Improved double-robust estimation in missing data and causal inference models |
title_sort |
improved double-robust estimation in missing data and causal inference models |
publishDate |
2012 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00063444_v99_n2_p439_Rotnitzky http://hdl.handle.net/20.500.12110/paper_00063444_v99_n2_p439_Rotnitzky |
work_keys_str_mv |
AT suedmariela improveddoublerobustestimationinmissingdataandcausalinferencemodels |
_version_ |
1768546751531188224 |