Learning causal polytrees

  • Juan F. Huete
  • Luis M. de Campos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 747)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Juan F. Huete
    • 1
  • Luis M. de Campos
    • 1
  1. 1.Dpto. de Ciencias de la Computacion e I.A.Universidad de GranadaGranadaSpain

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