Causality in Bayesian Belief Networks
This work addresses a foundational issue in probabilistic graphical models for researchers in AI and statistics, but it is incremental as it builds on existing structural equations models.
The paper tackles the problem of causal interpretation in Bayesian belief networks (BBNs) by linking them to structural equations models, resulting in a mechanism-based view that defines causality within models and formulates conditions for causal interpretation.
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.