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 conte...
