  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.
