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dc.contributor.authorHelske, Jouni
dc.contributor.authorTikka, Santtu
dc.contributor.authorKarvanen, Juha
dc.date.accessioned2021-05-24T08:09:42Z
dc.date.available2021-05-24T08:09:42Z
dc.date.issued2021
dc.identifier.citationHelske, J., Tikka, S., & Karvanen, J. (2021). Estimation of causal effects with small data in the presence of trapdoor variables. <i>Journal of the Royal Statistical Society. Series A: Statistics in Society</i>, <i>184</i>(3), 1030-1051. <a href="https://doi.org/10.1111/rssa.12699" target="_blank">https://doi.org/10.1111/rssa.12699</a>
dc.identifier.otherCONVID_86927238
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/75873
dc.description.abstractWe consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functional constraint that is not deducible from conditional independence relations, the estimator of the causal effect can exhibit bias in small samples. This bias is related to variables that we call trapdoor variables. We use simulated data to study different strategies to account for trapdoor variables and suggest how the related trapdoor bias might be minimized. The importance of trapdoor variables in causal effect estimation is illustrated with real data from the Life Course 1971–2002 study. Using this data set, we estimate the causal effect of education on income in the Finnish context. Bayesian modelling allows us to take the parameter uncertainty into account and to present the estimated causal effects as posterior distributions.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofseriesJournal of the Royal Statistical Society. Series A: Statistics in Society
dc.rightsCC BY 4.0
dc.subject.otherBayesian estimation
dc.subject.otherbias
dc.subject.othercausality
dc.subject.otherfunctional constraint
dc.subject.otheridentifiability
dc.titleEstimation of causal effects with small data in the presence of trapdoor variables
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202105243133
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1030-1051
dc.relation.issn0964-1998
dc.relation.numberinseries3
dc.relation.volume184
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysoestimointi
dc.subject.ysobayesilainen menetelmä
dc.subject.ysokausaliteetti
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p11349
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p333
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1111/rssa.12699
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationAcademy of Finland, Grant/Award Number: 311877


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