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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
H. B. Asher83. Causal modeling. Sage Publications, Beverly Hills, 1983.
P. Blau and O. Duncan. The American occupational structure. Wiley, New York, 1967.
John C. leohlin. Latent Variable Models: An Introduction to Factor Path and Structure Analysis. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1992.
Judea Pearl. On the connection between the complexity and credibility of inferred models. International Journal of General Systems, 4:255–264, 1978.
K. R. Popper. The Logic of Scientific Discovery. Basic Books, New York, 1959.
Peter Spirtes, Clark Glymour, and Richard Scheines. Causality from probability. In J.E. Tiles, G.T. McKee, and G.C. Dean, editors, Evolving Knowledge in Natural Science and Artificial Intelligence, London, 1990. Pitman.
Chris Wallace, Kevin Korb, and Honghua Dai. Causal discovery via MML. Technical report, Department of Computer Science, Monash University, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-48912-6_54
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65866-5
Online ISBN: 978-3-540-48912-2
eBook Packages: Springer Book Archive