Abstract

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. 1 INTRODUCTION Although references to causality permeate everyday scientific practice, the notion of causation has been one of the most controversial subjects in the philosophy of science. Hume's critique that causal connections cannot be observed, and therefore have no empirical basis, strongly influenced the empiricist framework and refocused the concept of causality to scientific models as opposed to reality. A strong attack on ca...

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