  Most traditional models of uncertainty have focused on the associational
relationship among variables as captured by conditional dependence. In order to
successfully manage intelligent systems for decision making, however, we must
be able to predict the effects of actions. In this paper, we attempt to unite
two branches of research that address such predictions: causal modeling and
decision analysis. First, we provide a definition of causal dependence in
decision-analytic terms, which we derive from consequences of causal dependence
cited in the literature. Using this definition, we show how causal dependence
can be represented within an influence diagram. In particular, we identify two
inadequacies of an ordinary influence diagram as a representation for cause. We
introduce a special class of influence diagrams, called causal influence
diagrams, which corrects one of these problems, and identify situations where
the other inadequacy can be eliminated. In addition, we describe the
relationships between Howard Canonical Form and existing graphical
representations of cause.
