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Front Genet. 2019 Jun 4;10:524. doi: 10.3389/fgene.2019.00524. eCollection 2019.

Review of Causal Discovery Methods Based on Graphical Models.

Author information

1
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, United States.

Abstract

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.

KEYWORDS:

causal discovery; conditional independence; directed graphical causal models; non-Gaussian distribution; non-linear models; statistical independence; structural equation models

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