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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Veröffentlicht in:Frontiers in genetics Jg. 10; S. 524
Hauptverfasser: Glymour, Clark, Zhang, Kun, Spirtes, Peter
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 04.06.2019
Schlagworte:
causal discovery
conditional independence
directed graphical causal models
Genetics
non-Gaussian distribution
statistical independence
structural equation models
conditional independence
directed graphical causal models
causal discovery
statistical independence
non-Gaussian distribution
structural equation models
non-linear models
ISSN:1664-8021, 1664-8021
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Zusammenfassung: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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Edited by: Momiao Xiong, University of Texas Health Science Center, United States
Reviewed by: Paola Sebastiani, Boston University, United States; Shaoyu Li, University of North Carolina at Charlotte, United States
This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2019.00524