Bayesian Network Learning with Discrete Case-Control Data

摘要

We address the problem of learning Bayesian networks from discrete, unmatched case-control data using specialized conditional independence tests. Those tests can also be used for learning other types of graphical models or for feature selection. We also propose a post-processing method that can be applied in conjunction with any Bayesian network learning algorithm. In simulations we show that our methods are able to deal with selection bias from case-control data.