Näytä suppeat kuvailutiedot

dc.contributor.authorTikka, Santtu
dc.contributor.authorHyttinen, Antti
dc.contributor.authorKarvanen, Juha
dc.date.accessioned2021-10-05T09:18:45Z
dc.date.available2021-10-05T09:18:45Z
dc.date.issued2021
dc.identifier.citationTikka, S., Hyttinen, A., & Karvanen, J. (2021). Causal Effect Identification from Multiple Incomplete Data Sources : A General Search-Based Approach. <i>Journal of Statistical Software</i>, <i>99</i>, Article 5. <a href="https://doi.org/10.18637/jss.v099.i05" target="_blank">https://doi.org/10.18637/jss.v099.i05</a>
dc.identifier.otherCONVID_101358555
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78024
dc.description.abstractCausal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature such as combined transportability and selection bias, or multiple sources of selection bias. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to the generality of do-calculus, the search is capable of taking more advanced datagenerating mechanisms into account along with an arbitrary type of both observational and experimental source distributions. The search is enhanced via a heuristic and search space reduction techniques. The approach, called do-search, is provably sound, and it is complete with respect to identifiability problems that have been shown to be completely characterized by do-calculus. When extended with additional rules, the search is capable of handling missing data problems as well. With the versatile search, we are able to approach new problems for which no other algorithmic solutions exist. We perform a systematic analysis of bivariate missing data problems and study causal inference under case-control design. We also present the R package dosearch that provides an interface for a C++ implementation of the search.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherFoundation for Open Access Statistic
dc.relation.ispartofseriesJournal of Statistical Software
dc.rightsCC BY 4.0
dc.subject.othercausality
dc.subject.otherdo-calculus
dc.subject.otherselection bias
dc.subject.othertransportability
dc.subject.othermissing data
dc.subject.othercase-control design
dc.subject.othermeta-analysis
dc.titleCausal Effect Identification from Multiple Incomplete Data Sources : A General Search-Based Approach
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202110055076
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.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn1548-7660
dc.relation.volume99
dc.type.versionpublishedVersion
dc.rights.copyright© Authors, 2021
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysokausaliteetti
dc.subject.ysoR-kieli
dc.subject.ysometa-analyysi
dc.subject.ysohakualgoritmit
dc.subject.ysopäättely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p333
jyx.subject.urihttp://www.yso.fi/onto/yso/p24355
jyx.subject.urihttp://www.yso.fi/onto/yso/p27697
jyx.subject.urihttp://www.yso.fi/onto/yso/p37865
jyx.subject.urihttp://www.yso.fi/onto/yso/p5902
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.18637/jss.v099.i05
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramProfilointi, SAfi
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). AH was supported by Academy of Finland through grant 295673.
dc.type.okmA1


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot

CC BY 4.0
Ellei muuten mainita, aineiston lisenssi on CC BY 4.0