Causality in Bayesian belief networks

Authors:
Marek J. Druzdzel;Herbert A. Simon
Affiliations:
Carnegie Mellon University, Department of Engineering and Public Policy, Pittsburgh, PA;Carnegie Mellon University, Department of Psychology, Pittsburgh, PA
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

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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.