Causal Discovery via MML.

@inproceedings{Wallace1996CausalDV,
  title={Causal Discovery via MML.},
  author={Chris S. Wallace and Kevin B. Korb and Honghua Dai},
  booktitle={ICML 1996},
  year={1996}
}
Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 29 references

Estimation and inference by compact coding

  • Chris Wallace, P. R. Freeman
  • Journal of the Royal Statistical Society, B,
  • 1987
Highly Influential
4 Excerpts

Khachiyan . On the conductance of order markov chains

  • A. Karzanov, L.
  • 1995

MML and Bayesianism: Similarities and di erences

  • J. J. Oliver, R. A. Baxter
  • Tech- nical Report TR 94/206,
  • 1994
1 Excerpt

TETRAD II: Tools for causal modeling

  • Peter Spirtes, Clark Glymour, Richard Scheines, C. Meek
  • 1994
2 Excerpts

Causation, Prediction, and Search

  • Peter Spirtes, Clark Glymour, Richard Scheines
  • 1993
1 Excerpt

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