Abstract
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.
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© 1994 Springer-Verlag New York, Inc.
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Esposito, F., Malerba, D., Semeraro, G. (1994). Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods. In: Cheeseman, P., Oldford, R.W. (eds) Selecting Models from Data. Lecture Notes in Statistics, vol 89. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2660-4_24
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DOI: https://doi.org/10.1007/978-1-4612-2660-4_24
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