On the modeling of causal belief networks

Causality is compactly and simply represented with graphical models. On these causal networks, we can compute the simultaneous effect of observing the natural behavior of the system and external actions forcing some variables to take specific values. This paper proposes an alternative causal graphical model offering more flexibility and reducing the storage complexity under an uncertain environment where the uncertainty is represented by belief assignments, the so-called causal belief network with conditional beliefs. Indeed, in this representation conditional distributions are defined for either one or more than one cause. To compute the global joint distribution on this network, we also propose a new method for the vacuous extension allowing a uniform transfer of beliefs.