Abstract
The essence of causality can be identified with a graphical structure representing relevance relationships between variables. In this paper the problem of infering causal relations from patterns of dependence is considered. We suppose that there exists a causal model, which is representable by a polytree structure and present an approach to the recovering problem. With this approach we can recover efficiently a polytree structure using marginal and conditional independence tests.
This work has been supported by the European Economic Community under Project Esprit III b.r.a. 6156 (DRUMS II)
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Huete, J.F., de Campos, L.M. (1993). Learning causal polytrees. In: Clarke, M., Kruse, R., Moral, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1993. Lecture Notes in Computer Science, vol 747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028199
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DOI: https://doi.org/10.1007/BFb0028199
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