Explaining Deep Tractable Probabilistic Models: The sum-product network case

Abstract

1 We consider the problem of explaining a tractable deep probabilistic model, the Sum-Product Networks (SPNs). To this effect, we define the notion of a context-specific independence tree and present an iterative algorithm that converts an SPN to a CSI-tree. The resulting CSI-tree is both interpretable and explainable to the domain expert. To further compress the tree, we approximate the CSIs by fitting a supervised classifier. Our extensive empirical evaluations on synthetic, standard, and real-world clinical data sets demonstrate that the resulting models exhibit superior explainability without loss in performance.