A Bayesian Network Approach for Analyzing Causal Relationships of Questions in Alcoholism Questionnaire Survey

This paper investigates a probabilistic causation problem that causes increase the probabilities of their effects on a suitable probabilistic model. A Bayesian network is used to analyze causal relationships of questions in Korean alcoholism questionnaire survey. We made a Korean obsessive compulsive drinking scale questionnaire for college students, and collected 73 responses to 21 questions over the course of an interview with investigating personnel from three different groups of responders. To identify and justify the casual relationships among questions we built an intial Bayesian network with an expert point of view’s network structure. However, G-test analysis of the variables, survey questions, with sample data revealed several discrepancies from the initial Bayesian network regarding relationship between nodes and also introduced an isolated subgraph. Interestingly, 3 potential arcs were identified and used to construct a modified Bayesian network which best correspond to the intention of the alcoholism questionnaire. Future works will assess the significance of these potential arcs with larger data sets and investigate whether revision of the alcoholism questionnaire is necessary for better diagnosis.