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dc.contributor.authorTikka, Santtu
dc.contributor.authorKarvanen, Juha
dc.date.accessioned2019-04-02T08:52:56Z
dc.date.available2021-05-02T21:35:08Z
dc.date.issued2019
dc.identifier.citationTikka, S., & Karvanen, J. (2019). Surrogate outcomes and transportability. <i>International Journal of Approximate Reasoning</i>, <i>108</i>. <a href="https://doi.org/10.1016/j.ijar.2019.02.007" target="_blank">https://doi.org/10.1016/j.ijar.2019.02.007</a>
dc.identifier.otherCONVID_28971132
dc.identifier.otherTUTKAID_80963
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/63333
dc.description.abstractIdentification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments [1] and we label it as surrogate outcome identifiability. We show that the concept of transportability [2] provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesInternational Journal of Approximate Reasoning
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherdo-calculus
dc.subject.othergraph
dc.subject.otheridentifiability
dc.subject.othermediator
dc.titleSurrogate outcomes and transportability
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201903121836
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.date.updated2019-03-12T16:15:15Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0888-613X
dc.relation.numberinseries0
dc.relation.volume108
dc.type.versionacceptedVersion
dc.rights.copyright© 2019 Elsevier Inc.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysokokeilu
dc.subject.ysopäättely
dc.subject.ysoverkkoteoria
dc.subject.ysoalgoritmit
dc.subject.ysokausaliteetti
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13984
jyx.subject.urihttp://www.yso.fi/onto/yso/p5902
jyx.subject.urihttp://www.yso.fi/onto/yso/p2543
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p333
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.ijar.2019.02.007
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationThis work belongs to the thematic research area “Decision analytics utilizing causal models and multiobjective optimization” (DEMO) supported by Academy of Finland (grant number 311877). We thank the anonymous reviewers for their comments which helped to substantially improve this paper.
dc.type.okmA1


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