dc.contributor.author | Tikka, Santtu | |
dc.contributor.author | Karvanen, Juha | |
dc.date.accessioned | 2019-04-02T08:52:56Z | |
dc.date.available | 2021-05-02T21:35:08Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Tikka, 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.other | CONVID_28971132 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/63333 | |
dc.description.abstract | Identification 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.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Elsevier | |
dc.relation.ispartofseries | International Journal of Approximate Reasoning | |
dc.rights | CC BY-NC-ND 4.0 | |
dc.subject.other | do-calculus | |
dc.subject.other | graph | |
dc.subject.other | identifiability | |
dc.subject.other | mediator | |
dc.title | Surrogate outcomes and transportability | |
dc.type | research article | |
dc.identifier.urn | URN:NBN:fi:jyu-201903121836 | |
dc.contributor.laitos | Matematiikan ja tilastotieteen laitos | fi |
dc.contributor.laitos | Department of Mathematics and Statistics | en |
dc.contributor.oppiaine | Tilastotiede | fi |
dc.contributor.oppiaine | Statistics | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.date.updated | 2019-03-12T16:15:15Z | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 0888-613X | |
dc.relation.numberinseries | 0 | |
dc.relation.volume | 108 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2019 Elsevier Inc. | |
dc.rights.accesslevel | openAccess | fi |
dc.type.publication | article | |
dc.relation.grantnumber | 311877 | |
dc.subject.yso | kokeilu | |
dc.subject.yso | päättely | |
dc.subject.yso | verkkoteoria | |
dc.subject.yso | algoritmit | |
dc.subject.yso | kausaliteetti | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p13984 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p5902 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p2543 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p14524 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p333 | |
dc.rights.url | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.relation.doi | 10.1016/j.ijar.2019.02.007 | |
dc.relation.funder | Suomen Akatemia | fi |
dc.relation.funder | Research Council of Finland | en |
jyx.fundingprogram | Profilointi, SA | fi |
jyx.fundingprogram | Research profiles, AoF | en |
jyx.fundinginformation | This 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.okm | A1 | |