Causality in Bayesian Belief Networks

2013/03/06 by Marek J. Drużdżel, Herbert A. Simon, Druzdzel, Marek J. +1 · 1 citation
Computer Science · #Bayesian Modeling and Causal Inference #Cognitive Science and Mapping #Logic, Reasoning, and Knowledge

paper · pdf · doi:10.48550/arxiv.1303.1454

Abstract

We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mechanisms are placed in the context of a system. We lay the link between structural equations models and BBNs models and formulate the conditions under which the latter can be given causal interpretation.

Citations

• The epistemology of a rule-based expert system —a framework for explanation

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