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.
A method for uncovering deterministic causal structures is introduced in REF .
Uncovering deterministic causal structures: a Boolean approach
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Mathematics Computer Science