Uncovering deterministic causal structures: a Boolean approach |
| |
Authors: | Michael Baumgartner |
| |
Affiliation: | (1) University of Bern, Bern, Switzerland |
| |
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. |
| |
Keywords: | Causation Causal reasoning Discovery algorithms Deterministic structures |
本文献已被 SpringerLink 等数据库收录! |
|