Learning of causal relations
John Quinn, Joris Mooij, Tom Heskes and Michael Biehl
In: 19th European Symposium on Artificial Neural Networks (ESANN 2011), April 27-29, 2011, Bruges, Belgium.

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.

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