Causal Modeling
This work addresses the need for more intuitive and easier-to-build models in fields like AI and statistics, though it appears incremental as it builds upon existing concepts like Dependency Graphs.
The paper tackles the problem of modeling complex hierarchical and parallel processes by introducing Causal Models as a more modular and intuitive alternative to Dependency Graphs, showing that Dependency Graph Models are a special case of them and presenting algorithms for inference and probability elicitation.
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.