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Uncovering Deterministic Causal Structures: A Boolean Approach

Michael Baumgartner
Synthese
Vol. 170, No. 1 (Sep., 2009), pp. 71-96
Published by: Springer
Stable URL: http://www.jstor.org/stable/40271344
Page Count: 26
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Uncovering Deterministic Causal Structures: A Boolean Approach
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Abstract

While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned to the minimalized deterministic dependencies.

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