Uncovering deterministic causal structures: a Boolean approach.

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 p...

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Publicado en:Synthese Vol. 170; no. 1; pp. 71 - 97
Autor principal: Baumgartner, Michael
Formato: Artículo
Publicado: Springer Nature Sep2009
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Uncovering deterministic causal structures: a Boolean approach.
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        au: Baumgartner, Michael
        affil: University of Bern, Bern Switzerland
      su:
        Methodology
        Bayesian analysis
        Probability theory
        Boolean matrices
        Reasoning
        Philosophical analysis
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          Methodology
          Bayesian analysis
          Probability theory
          Boolean matrices
          Reasoning
          Philosophical analysis
      keyword:
        Causal reasoning
        Causation
        Deterministic structures
        Discovery algorithms
      ab: 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.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Synthese is a copyright of Springer, 2009. All Rights Reserved.
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