Abstract
Many algorithms have been proposed for learning a causal network from data. It has been shown, however, that learning all the conditional independencies in a probability distribution is a NP-hard problem. In this chapter, we present an alternative method for learning a causal network from data. Our approach is novel in that it learns functional dependencies in the sample distribution rather than probabilistic independencies. Our method is based on the fact that functional dependency logically implies probabilistic conditional independency. The effectiveness of the proposed approach is explicitly demonstrated using fifteen real-world datasets.
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© 2005 Springer Science+Business Media, Inc.
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Yao, H., Butz, C.J., Hamilton, H.J. (2005). Causal Discovery. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_44
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DOI: https://doi.org/10.1007/0-387-25465-X_44
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-24435-8
Online ISBN: 978-0-387-25465-4
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