  The causal Bayesian approach is based on the assumption that effects (e.g.,
symptoms) that are not conditionally independent with respect to some causal
agent (e.g., a disease) are conditionally independent with respect to some
intermediate state caused by the agent, (e.g., a pathological condition). This
paper describes the development of a causal Bayesian model for the diagnosis of
appendicitis. The paper begins with a description of the standard Bayesian
approach to reasoning about uncertainty and the major critiques it faces. The
paper then lays the theoretical groundwork for the causal extension of the
Bayesian approach, and details specific improvements we have developed. The
paper then goes on to describe our knowledge engineering and implementation and
the results of a test of the system. The paper concludes with a discussion of
how the causal Bayesian approach deals with the criticisms of the standard
Bayesian model and why it is superior to alternative approaches to reasoning
about uncertainty popular in the Al community.
