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Identifiability and transportability in dynamic causal networks
dc.contributor.author | Blondel, Gilles |
dc.contributor.author | Arias Vicente, Marta |
dc.contributor.author | Gavaldà Mestre, Ricard |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2018-03-09T08:58:34Z |
dc.date.available | 2018-03-09T08:58:34Z |
dc.date.issued | 2017-03 |
dc.identifier.citation | Blondel, G., Arias, M., Gavaldà, R. Identifiability and transportability in dynamic causal networks. "International journal of data science and analytics", Març 2017, vol. 3, núm. 2, p. 131-147. |
dc.identifier.issn | 2364-415X |
dc.identifier.other | https://arxiv.org/abs/1610.05556v1 |
dc.identifier.uri | http://hdl.handle.net/2117/114969 |
dc.description.abstract | In this paper, we propose a causal analog to the purely observational dynamic Bayesian networks, which we call dynamic causal networks. We provide a sound and complete algorithm for the identification of causal effects in dynamic causal networks, namely for computing the effect of an intervention or experiment given a dynamic causal network and probability distributions of passive observations of its variables, whenever possible. We note the existence of two types of hidden confounder variables that affect in substantially different ways the identification procedures, a distinction with no analog in either dynamic Bayesian networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in dynamic causal network settings, where the result of causal experiments in a source domain may be used for the identification of causal effects in a target domain. |
dc.format.extent | 17 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica |
dc.subject.lcsh | Bayesian statistical decision theory |
dc.subject.lcsh | Graph theory |
dc.subject.other | Causal analysis |
dc.subject.other | Dynamic modeling |
dc.subject.other | Do-calculus |
dc.subject.other | Graphical models |
dc.subject.other | Confounding |
dc.title | Identifiability and transportability in dynamic causal networks |
dc.type | Article |
dc.subject.lemac | Estadística bayesiana |
dc.subject.lemac | Grafs, Teoria de |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.identifier.doi | 10.1007/s41060-016-0028-8 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s41060-016-0028-8 |
dc.rights.access | Open Access |
local.identifier.drac | 21980307 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Blondel, G.; Arias, M.; Gavaldà, R. |
local.citation.publicationName | International journal of data science and analytics |
local.citation.volume | 3 |
local.citation.number | 2 |
local.citation.startingPage | 131 |
local.citation.endingPage | 147 |
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