Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
The causal Markov condition, fact or artifact?
ACM SIGART Bulletin
Foundations of Fuzzy Systems
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
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In this paper we consider the problem of inducing causal relations from statistical data. Although it is well known that a correlation does not justify the claim of a causal relation between two measures, the question seems not to be settled. Research in the field of Bayesian networks revived an approach suggested in [16]. It is based on the idea that there are relationships between the causal structure of a domain and its corresponding probability distribution, which could be exploited to infer at least part of the causal structure from a set of dependence and independence statements. This idea was developed into the inductive causation algorithm [14]. We review this algorithm and examine the assumptions underlying it.