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dc.contributor.authorKarvanen, Juha
dc.contributor.authorTikka, Santtu
dc.contributor.authorVihola, Matti
dc.date.accessioned2024-07-08T06:47:59Z
dc.date.available2024-07-08T06:47:59Z
dc.date.issued2024
dc.identifier.citationKarvanen, J., Tikka, S., & Vihola, M. (2024). Simulating Counterfactuals. <i>Journal of Artificial Intelligence Research</i>, <i>80</i>, 835-857. <a href="https://doi.org/10.1613/jair.1.15579" target="_blank">https://doi.org/10.1613/jair.1.15579</a>
dc.identifier.otherCONVID_220936808
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/96324
dc.description.abstractCounterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables. We show that the proposed algorithm can be presented as a particle filter leading to asymptotically valid inference. The algorithm is applied to fairness analysis in credit-scoring.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherAI Access Foundation
dc.relation.ispartofseriesJournal of Artificial Intelligence Research
dc.rightsCC BY 4.0
dc.subject.otherbayesian networks
dc.subject.othercausality
dc.subject.othermachine learning
dc.titleSimulating Counterfactuals
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202407085151
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange835-857
dc.relation.issn1076-9757
dc.relation.volume80
dc.type.versionpublishedVersion
dc.rights.copyright©2024 The Authors. Published by AI Access Foundation
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber346311
dc.relation.grantnumber331817
dc.subject.ysoalgoritmit
dc.subject.ysokausaliteetti
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p333
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1613/jair.1.15579
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramCentre of Excellence, AoFen
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramHuippuyksikkörahoitus, SAfi
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationCSC – IT Center for Science, Finland, is acknowledged for computational resources. MV was supported by Research Council of Finland (Finnish Centre of Excellence in Randomness and Structures, grant 346311). ST was supported by Research Council of Finland (PREDLIFE: Towards well-informed decisions: Predicting long-term effects of policy re-forms on life trajectories, grant 331817).
dc.type.okmA1


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