A generic qualitative characterization of independence of causal influence

AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of Bayesian networks. However, these models are not as widely applied as they could, as their behavior is often not well-understood. One approach is to employ qualitative probabilistic network theory in order to derive a qualitative characterization of ICI models. In this paper we analyze the qualitative properties of ICI models with binary random variables. Qualitative properties are shown to follow from the characteristics of the Boolean function underlying the model. In addition, it is demonstrated that the theory also allows finding constraints on the model parameters given knowledge of the qualitative properties. This high-level qualitative characterization offers a new way of identifying suitable ICI models and may facilitate their exploitation in developing real-world Bayesian networks