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dc.contributor.authorTikka, Santtu
dc.contributor.authorKarvanen, Juha
dc.date.accessioned2018-07-10T05:21:07Z
dc.date.available2018-07-10T05:21:07Z
dc.date.issued2018
dc.identifier.citationTikka, S., & Karvanen, J. (2018). Enhancing Identification of Causal Effects by Pruning. <i>Journal of Machine Learning Research</i>, <i>18</i>, 1-23. <a href="http://www.jmlr.org/papers/volume18/17-563/17-563.pdf" target="_blank">http://www.jmlr.org/papers/volume18/17-563/17-563.pdf</a>
dc.identifier.otherCONVID_28149935
dc.identifier.otherTUTKAID_78184
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/58881
dc.description.abstractCausal models communicate our assumptions about causes and e ects in real-world phenomena. Often the interest lies in the identification of the e ect of an action which means deriving an expression from the observed probability distribution for the interventional distribution resulting from the action. In many cases an identifiability algorithm may return a complicated expression that contains variables that are in fact unnecessary. In practice this can lead to additional computational burden and increased bias or ine ciency of estimates when dealing with measurement error or missing data. We present graphical criteria to detect variables which are redundant in identifying causal e ects. We also provide an improved version of a well-known identifiability algorithm that implements these criteria.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMIT Press
dc.relation.ispartofseriesJournal of Machine Learning Research
dc.relation.urihttp://www.jmlr.org/papers/volume18/17-563/17-563.pdf
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.othercausal inference
dc.subject.otheridentiafiability
dc.subject.othercausal model
dc.subject.otheralgorithm
dc.titleEnhancing Identification of Causal Effects by Pruning
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201807043469
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.updated2018-07-04T06:15:10Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1-23
dc.relation.issn1532-4435
dc.relation.numberinseries0
dc.relation.volume18
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysopäättely
dc.subject.ysotunnistaminen
dc.subject.ysoalgoritmit
dc.subject.ysoleikkaus (kasvit)
dc.subject.ysokoneoppiminen
dc.subject.ysokausaliteetti
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5902
jyx.subject.urihttp://www.yso.fi/onto/yso/p8265
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p29739
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p333
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationWe wish to thank Professor Jukka Nyblom for his comments that greatly helped to improve this paper. We also thank the anonymous reviewers for their insightful feedback. The work belongs to the profiling area ”Decision analytics utilizing causal models and multiobjective optimization” (DEMO) supported by Academy of Finland (grant number 311877).
dc.type.okmA1


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