Causality in Bayesian Belief Networks

We address the problem of causal interpre­ tation of the graphical structure of Bayesian belief networks (BBNs). We review the con­ cept 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 for­ mulate the conditions under which the latter can be given causal interpretation.