Causal Modeling

TL;DR

Causal Models extend dependency graphs by representing hierarchical and parallel processes, offering more modular, intuitive, and easier-to-understand structures with formal definitions and inference algorithms.

Contribution

The paper formally defines Causal Models, demonstrates their generality over Dependency Graphs, and introduces algorithms and methods for inference and probability elicitation.

Findings

Causal Models are more modular and intuitive than Dependency Graphs.

Dependency Graphs are a special case of Causal Models.

Algorithms for inference in Causal Models are developed.

Abstract

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.