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A Minimal Causal Model Learner

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Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

The minimal-model semantics of causation is an essential concept for the identification of a best fitting model in the sense of satisfactory consistent with the given data and be the simpler, less expressive model. Therefore to develop an algorithm being able to derive a minimal model is an interesting topic in the area of causal model discovery. various causal induction algorithms and tools developed so far can not guarantee that the derived model is a minimal model. This paper proves that the MML induction approach introduced by Wallace, et al is a minimal causal model learner. The experimental results obtained from the tests on a number of both artificial and real models provided in this paper confirm this theoretical result.

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References

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Deakin University, 662 Blackburn Road, Vic, 3168, Australia

    Honghua Dai

Authors
  1. Honghua Dai
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Dai, H. (1999). A Minimal Causal Model Learner. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_54

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  • DOI: https://doi.org/10.1007/3-540-48912-6_54

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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