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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Autores principales: Rotnitzky, A., Lei, Q., Sued, M., Robins, J.M.
Formato: JOUR
Materias:
Drop-out
Marginal structural model
Missing at random
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00063444_v99_n2_p439_Rotnitzky
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00063444_v99_n2_p439_Rotnitzky
record_format dspace
spelling todo:paper_00063444_v99_n2_p439_Rotnitzky2023-10-03T14:05:01Z Improved double-robust estimation in missing data and causal inference models Rotnitzky, A. Lei, Q. Sued, M. Robins, J.M. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Rotnitzky, A.
Lei, Q.
Sued, M.
Robins, J.M.
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.
format JOUR
author Rotnitzky, A.
Lei, Q.
Sued, M.
Robins, J.M.
author_facet Rotnitzky, A.
Lei, Q.
Sued, M.
Robins, J.M.
author_sort Rotnitzky, A.
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
url http://hdl.handle.net/20.500.12110/paper_00063444_v99_n2_p439_Rotnitzky
work_keys_str_mv AT rotnitzkya improveddoublerobustestimationinmissingdataandcausalinferencemodels
AT leiq improveddoublerobustestimationinmissingdataandcausalinferencemodels
AT suedm improveddoublerobustestimationinmissingdataandcausalinferencemodels
AT robinsjm improveddoublerobustestimationinmissingdataandcausalinferencemodels
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