Improved double-robust estimation in missing data and causal inference models

Autores
Rotnitzky, Andrea Gloria; Lei, Quanhong; Sued, Raquel Mariela; Robins, James M.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella. Departamento de Economía; Argentina
Fil: Lei, Quanhong. Harvard University; Estados Unidos
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Robins, James M.. Harvard University; Estados Unidos
Materia
Drop-Out
Marginal Structural Model
Missing at Random
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/68404
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Improved double-robust estimation in missing data and causal inference modelsRotnitzky, Andrea GloriaLei, QuanhongSued, Raquel MarielaRobins, James M.Drop-OutMarginal Structural ModelMissing at Randomhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Recently 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.Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella. Departamento de Economía; ArgentinaFil: Lei, Quanhong. Harvard University; Estados UnidosFil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Robins, James M.. Harvard University; Estados UnidosOxford University Press2012-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/68404Rotnitzky, Andrea Gloria; Lei, Quanhong; Sued, Raquel Mariela; Robins, James M.; Improved double-robust estimation in missing data and causal inference models; Oxford University Press; Biometrika; 99; 2; 6-2012; 439-4560006-3444CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/ass013info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/biomet/article-abstract/99/2/439/306137info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:52:41Zoai:ri.conicet.gov.ar:11336/68404instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:52:41.801CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improved double-robust estimation in missing data and causal inference models
title Improved double-robust estimation in missing data and causal inference models
spellingShingle Improved double-robust estimation in missing data and causal inference models
Rotnitzky, Andrea Gloria
Drop-Out
Marginal Structural Model
Missing at Random
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
dc.creator.none.fl_str_mv Rotnitzky, Andrea Gloria
Lei, Quanhong
Sued, Raquel Mariela
Robins, James M.
author Rotnitzky, Andrea Gloria
author_facet Rotnitzky, Andrea Gloria
Lei, Quanhong
Sued, Raquel Mariela
Robins, James M.
author_role author
author2 Lei, Quanhong
Sued, Raquel Mariela
Robins, James M.
author2_role author
author
author
dc.subject.none.fl_str_mv Drop-Out
Marginal Structural Model
Missing at Random
topic Drop-Out
Marginal Structural Model
Missing at Random
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella. Departamento de Economía; Argentina
Fil: Lei, Quanhong. Harvard University; Estados Unidos
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Robins, James M.. Harvard University; Estados Unidos
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.
publishDate 2012
dc.date.none.fl_str_mv 2012-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/68404
Rotnitzky, Andrea Gloria; Lei, Quanhong; Sued, Raquel Mariela; Robins, James M.; Improved double-robust estimation in missing data and causal inference models; Oxford University Press; Biometrika; 99; 2; 6-2012; 439-456
0006-3444
CONICET Digital
CONICET
url http://hdl.handle.net/11336/68404
identifier_str_mv Rotnitzky, Andrea Gloria; Lei, Quanhong; Sued, Raquel Mariela; Robins, James M.; Improved double-robust estimation in missing data and causal inference models; Oxford University Press; Biometrika; 99; 2; 6-2012; 439-456
0006-3444
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/ass013
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/biomet/article-abstract/99/2/439/306137
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269176611209216
score 13.13397

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