An Empirical Investigation of Instance-Specific Causal Bayesian Network Learning

Significant progress has been made in developing algorithms for learning graphical causal models from data. Most of these algorithms learn a causal structure that is shared by all the instances (e.g., patients) in the training dataset. However, different instances may not all share the same causal structure. We introduced an instance-specific method called IGES [15] that learns a causal model for each instance T by using the features of T and the instances in the training dataset. In the current paper, we study the empirical performance of the IGES method on several biomedical datasets. The results provide support that instance-specific structure exists and is important to model in these real domains.