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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)

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.

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References

  1. 1.
    Acid S., Campos L.M. de, Gonzalez A. Molina R., Pérez de la Blanca N.: Learning with CASTLE, in Symbolic and Quantitative approaches to Uncertainty. Lecture Notes in Computer Science 584, R. Kruse R., Siegel P. (Eds). Springer Verlag, (1991) 99–106.Google Scholar
  2. 2.
    Acid S., Campos L.M. de, González A. Molina R., Pérez de la Blanca N.: CASTLE: A tool for bayesian learning. Proceedings of the ESPRIT 91 Conference, Commission of the European Communities, (1991) 363–377.Google Scholar
  3. 3.
    Campos L.M.de, Huete J.: Independence concepts in Upper and Lower probabilities. Proceedings of the Fourth IPMU Conference, (1992) 129–132.Google Scholar
  4. 4.
    Chow C.K., Liu C.N.: Approximating discrete probability distribution with dependence trees. IEEE Transactions on Information Theory 14, (1968) 462–467.Google Scholar
  5. 5.
    Cooper G., Herskovits E.: A bayesian method for the induction of Probabilistic Networks from data. Machine Learning 9, (1992) 309–347.Google Scholar
  6. 6.
    Geiger D., Paz A., Pearl J.: Learning causal trees from Dependence information. Proceedings of the Eighth National Conference on A.I. (1990) 770–776.Google Scholar
  7. 7.
    Geiger D., Paz A., Pearl J.: Learning Simple Causal Structures International Journal of Intelligent Systems, Vol 8, (1993) 231–247.Google Scholar
  8. 8.
    Herskovits E., Cooper G.: KUTATO: An Entropy-Driven system for construction of probabilistic expert systems from Databases. Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence, Cambridge MA. (1990) 54–62.Google Scholar
  9. 9.
    Pearl J.: Probabilistic Reasoning in intelligent systems: Networks of plausible inference. Morgan and Kaufman, San Mateo (1988).Google Scholar
  10. 10.
    Rebane G., Pearl J.: The recovery of causal poly-trees from statistical data, Uncertainty in Artificial Intelligence 3, Kanal L.N., Levitt T.S. and Lemmer J.F. Eds. North-Holland. (1989) 175–182.Google Scholar
  11. 11.
    Srinivas S., Russell S., Agogino A.: Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information. Uncertainty in Artificial Intelligence 5, Henrion M., Shachter R.D., Kanal L.N., Lemmer J.F. (Eds), North-Holland, Amsterdam. (1990) 295–308.Google Scholar

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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