Abstract
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.
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Borgelt, C., Kruse, R. (1999). A Critique of Inductive Causation. In: Hunter, A., Parsons, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1999. Lecture Notes in Computer Science(), vol 1638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48747-6_7
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DOI: https://doi.org/10.1007/3-540-48747-6_7
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