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Inferring the Causal Decomposition under the Presence of Deterministic Relations Host Publication: Special session Learning of causal relations at the 18th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges (Belgium) Authors: J. Lemeire, S. Meganck, F. Cartella, T. Liu and A. Statnikov Publisher: ESANN Publication Date: Apr. 2011 Number of Pages: 6 ISBN: 978-2-87419-044-5
Abstract: The presence of deterministic relations pose problems for current algorithms
that learn the causal structure of a system based on the observed conditional
independencies. Deterministic variables lead to information equivalences two sets
of variables have the same information about a third variable. Based on information
content, one cannot decide on the direct causes. Several edges model equally well
the dependencies. We call them equivalent edges. We propose to select among
the equivalent edges the one with the simplest descriptive complexity. This approach
assumes that the descriptive complexity increases along a causal path. As
confirmed by our experimental results, the accuracy of the method depends on the
chance of accidental matches of complexities. External Link.
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