Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods
This paper presents a comparison between two different approaches to statistical causal inference, namely Glymour et al.’s approach based on constraints on correlations and Pearl and Verma’s approach based on conditional independencies. The methods differ both in the kind of constraints considered while selecting a causal model and in the way they search for the model which better fits the sample data. Some experiments show that they are complementary in several aspects.
KeywordsCovariance Kelly Verse
Unable to display preview. Download preview PDF.
- [Anderson 58]Anderson, T.W. (1958) An Introduction to Multivariate Statistical Analysis John Wiley and Sons, New York, NY.Google Scholar
- [GIShSpKe 87]Glymour, C., Schemes, R., Spirtes, R and K. Kelly (1987) Discovering Causal Structure Academic Press, Orlando, FL.Google Scholar
- [Pazzani 90]Pazzani, M.J. (1990) Creating a Memory of Causal Relationships: An Integration of Explanation-Based and Empirical Methods Erlbaum, Hillsdale, NJ.Google Scholar
- [PearVerm91]Pearl, J. and T.S. Verma (1991) “A theory of Inferred Causation,” in Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, J.A. Allen, R. Fikes, and E. Sandewall, eds., 441–452, Morgan Kaufmann, San Mateo, CA.Google Scholar
- [Quinlan 90]Quinlan, J. R. (1990) “Probabilistic Decision Trees,” in Machine Learning: An Artificial Intelligence Approach, vol III, Y. Kodratoff and R.S. Michalski, eds., 140–152, Morgan Kaufmann, San Mateo, CA.Google Scholar
- [Reichenb 56]Reichenbach, H. (1956) The Direction of Time, University of California Press, Berkley, CA.Google Scholar
- [Steels 85]
- [Suppes 70]Suppes, P. (1970) A Probabilistic Theory of Causation North Holland, Amsterdam, Holland.Google Scholar