Leveraging D-Separation for Relational Data Sets

Testing for marginal and conditional independence is a common task in machine learning and knowledge discovery applications. Prior work has demonstrated that conventional independence tests suffer from dramatically increased rates of Type I errors when naively applied to relational data. We use graphical models to specify the conditions under which these errors occur, and use those models to devise novel and accurate conditional independence tests.