dc.contributor.author | Tikka, Santtu | |
dc.contributor.author | Karvanen, Juha | |
dc.date.accessioned | 2017-03-07T08:13:34Z | |
dc.date.available | 2017-03-07T08:13:34Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Tikka, S., & Karvanen, J. (2017). Identifying Causal Effects with the R Package causaleffect. <i>Journal of Statistical Software</i>, <i>76</i>(12), 1-30. <a href="https://doi.org/10.18637/jss.v076.i12" target="_blank">https://doi.org/10.18637/jss.v076.i12</a> | |
dc.identifier.other | CONVID_26887029 | |
dc.identifier.other | TUTKAID_73145 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/53194 | |
dc.description.abstract | Do-calculus is concerned with estimating the interventional distribution of an action
from the observed joint probability distribution of the variables in a given causal structure.
All identifiable causal effects can be derived using the rules of do-calculus, but
the rules themselves do not give any direct indication whether the effect in question is
identifiable or not. Shpitser and Pearl (2006b) constructed an algorithm for identifying
joint interventional distributions in causal models, which contain unobserved variables
and induce directed acyclic graphs. This algorithm can be seen as a repeated application
of the rules of do-calculus and known properties of probabilities, and it ultimately either
derives an expression for the causal distribution, or fails to identify the effect, in which
case the effect is non-identifiable. In this paper, the R package causaleffect is presented,
which provides an implementation of this algorithm. Functionality of causaleffect is also
demonstrated through examples. | |
dc.language.iso | eng | |
dc.publisher | Foundation for Open Access Statistics | |
dc.relation.ispartofseries | Journal of Statistical Software | |
dc.subject.other | DAG | |
dc.subject.other | do-calculus | |
dc.subject.other | causal model | |
dc.subject.other | identifiability | |
dc.subject.other | graph | |
dc.subject.other | C-component | |
dc.subject.other | hedge | |
dc.subject.other | d-separation | |
dc.title | Identifying Causal Effects with the R Package causaleffect | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-201703021559 | |
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 | 2017-03-02T10:15:04Z | |
dc.type.coar | journal article | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 1-30 | |
dc.relation.issn | 1548-7660 | |
dc.relation.numberinseries | 12 | |
dc.relation.volume | 76 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © the Authors, 2017. This is an open access article distributed under a Creative Commons license. | |
dc.rights.accesslevel | openAccess | fi |
dc.subject.yso | kausaliteetti | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p333 | |
dc.rights.url | https://creativecommons.org/licenses/by/3.0/ | |
dc.relation.doi | 10.18637/jss.v076.i12 | |