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(2009) Synthese 170 (1).
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.
Publication details
DOI: 10.1007/s11229-008-9348-0
Full citation:
Baumgartner, M. (2009). Uncovering deterministic causal structures: a boolean approach. Synthese 170 (1), pp. 71-96.
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