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.
Causality in Bayesian Belief Networks