  Causal Models are like Dependency Graphs and Belief Nets in that they provide
a structure and a set of assumptions from which a joint distribution can, in
principle, be computed. Unlike Dependency Graphs, Causal Models are models of
hierarchical and/or parallel processes, rather than models of distributions
(partially) known to a model builder through some sort of gestalt. As such,
Causal Models are more modular, easier to build, more intuitive, and easier to
understand than Dependency Graph Models. Causal Models are formally defined and
Dependency Graph Models are shown to be a special case of them. Algorithms
supporting inference are presented. Parsimonious methods for eliciting
dependent probabilities are presented.
