A Probabilistic Causal Model for Diagnostic Problem Solving Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference

Abstract

The issue of how to effectively integrate and use symbolic causal knowledge with numeric estimates of probabilities in abductive diagnostic expert systems is examined. In particular, a formal probabilistic causal model that integrates Bayesian classification with a domain-independent artificial intelligence model of diagnostic problem solving (parsimonious covering theory) is developed. Through a careful analysis, it is shown that the causal relationships in a general diagnostic domain can be used to remove the barriers to applying Bayesian classification effectively (large number of probabilities required as part of the knowledge base, certain unrealistic independence assumptions, the explosion of diagnostic hypotheses that occurs when multiple disorders can occur simultaneously, etc.). Further, this analysis provides insight into which notions of "parsimony" may be relevant in a given application area. In a companion paper, Part Two, a computationally efficient diagnostic strategy based on the probabilistic causal model discussed in this paper is developed.