%A Glymour,Clark %A Zhang,Kun %A Spirtes,Peter %D 2019 %J Frontiers in Genetics %C %F %G English %K Directed graphical causal models,causal discovery,Conditional independence,Statistical independence,Structural Equation Models,non-Gaussian distribution,Non-linear models %Q %R 10.3389/fgene.2019.00524 %W %L %N 524 %M %P %7 %8 2019-June-04 %9 Review %# %! Review of Causal Discovery %* %< %T Review of Causal Discovery Methods Based on Graphical Models %U https://www.frontiersin.org/article/10.3389/fgene.2019.00524 %V 10 %0 JOURNAL ARTICLE %@ 1664-8021 %X 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.