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This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything useful about the causal mechanisms that underly the(More)
Dependency knowledge of the fonn "x is independent of y once z is known" invariably obeys a set of four axioms defining semi-graphoids, examples of which are probabilistic and database dependen­ cies. Such knowledge can often be stored efficiently in graphical structures, using either undirected graphs and directed acyclic graphs (DAGs) . This paper shows(More)
Data-dependencies of the type “x can tell us more about y given that we already know z ” can be represented in various formalisms: Probabilistic Dependencies, Embedded-Multi-Valued Dependencies, Undirected Graphs and Directed-Acyclic Graphs (DAGs). This paper provides an axiomatic basis, called a semigraph& which captures the structure common to all four(More)
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