A Causal Mapping Approach to Constructing Bayesian Networks

This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision.The research has been supported by two grants from the Kansas University Business School PhD Summer Research Fund to both authors and by a grant from the Kansas University General Research Fund to Prakash P. Shenoy