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Review of Causal Discovery Methods Based on Graphical Models

A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to d...

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Detalles Bibliográficos
Autores principales: Glymour, Clark, Zhang, Kun, Spirtes, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558187/
https://www.ncbi.nlm.nih.gov/pubmed/31214249
http://dx.doi.org/10.3389/fgene.2019.00524
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author Glymour, Clark
Zhang, Kun
Spirtes, Peter
author_facet Glymour, Clark
Zhang, Kun
Spirtes, Peter
author_sort Glymour, Clark
collection PubMed
description A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search. This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.
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spelling pubmed-65581872019-06-18 Review of Causal Discovery Methods Based on Graphical Models Glymour, Clark Zhang, Kun Spirtes, Peter Front Genet Genetics A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search. This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications. Frontiers Media S.A. 2019-06-04 /pmc/articles/PMC6558187/ /pubmed/31214249 http://dx.doi.org/10.3389/fgene.2019.00524 Text en Copyright © 2019 Glymour, Zhang and Spirtes. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Glymour, Clark
Zhang, Kun
Spirtes, Peter
Review of Causal Discovery Methods Based on Graphical Models
title Review of Causal Discovery Methods Based on Graphical Models
title_full Review of Causal Discovery Methods Based on Graphical Models
title_fullStr Review of Causal Discovery Methods Based on Graphical Models
title_full_unstemmed Review of Causal Discovery Methods Based on Graphical Models
title_short Review of Causal Discovery Methods Based on Graphical Models
title_sort review of causal discovery methods based on graphical models
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558187/
https://www.ncbi.nlm.nih.gov/pubmed/31214249
http://dx.doi.org/10.3389/fgene.2019.00524
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