Mining Causal Outliers Using Gaussian Bayesian Networks

Outliers are often identified as data points which are "rare'', "isolated'', or far away from their nearest neighbours. In this paper we demonstrate that meaningful outliers, i.e., outliers which perhaps encode important or new information are those which violate causal relationships. We first build a Bayesian network which encode causal relationships between attributes and then identify those points as outliers which violate these causal relationships. Experiments on several data sets confirm that the outliers identified in this fashion are in some sense "genuine'' as they reveal new information about the underlying data generating process.