Learning of Causal Relations
John A. Quinn, Joris M. Mooij, Tom Heskes, Michael L. Biehl · 2011
Abstract. To learn about causal relations between variables just by observing samples from them, particular assumptions must be made about those variables ’ distributions. This article gives a practical description of how such a learning task can be undertaken based on different possible assumptions. Two categories of assumptions lead to different methods, constraint-based and Bayesian learning, and in each case we review both the basic ideas and some recent extensions and alternatives to them. 1