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dc.contributor.authorYiu, Seanen
dc.contributor.authorSu, Lien
dc.date.accessioned2018-06-05T13:07:18Z
dc.date.available2018-06-05T13:07:18Z
dc.date.issued2018-09en
dc.identifier.issn0006-3444
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/276617
dc.description.abstractWeighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In 10 this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimation by eliminating the associations between these covariates and treatment assignment characterized in a chosen treatment assignment model after weighting. The moment conditions in covariate balancing weight methods for binary, categorical and continuous 15 treatments in cross-sectional settings are special cases of the conditions in our framework, which extends to longitudinal settings. Simulation shows that our method gives treatment effect estimates with smaller biases and variances than the maximum likelihood approach under treatment assignment model misspecification. We illustrate our method with an application in systemic lupus erythematosus.
dc.format.mediumPrinten
dc.languageengen
dc.publisherOxford University Press
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleCovariate association eliminating weights: a unified weighting framework for causal effect estimation.en
dc.typeArticle
prism.endingPage722
prism.issueIdentifier3en
prism.publicationDate2018en
prism.publicationNameBiometrikaen
prism.startingPage709
prism.volume105en
dc.identifier.doi10.17863/CAM.23917
dcterms.dateAccepted2018-01-26en
rioxxterms.versionofrecord10.1093/biomet/asy015en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-09en
dc.contributor.orcidSu, Li [0000-0003-0919-3462]
dc.identifier.eissn1464-3510
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (unknown)
cam.orpheus.successThu Jan 30 12:58:17 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International