Causality in Bayesian Belief Networks

Understand

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.