Counterfactual generating Variational Autoencoder for Anomaly Detection

Machine learning applications in fields such as financial accounting or the healthcare industry have to meet high transparency requirements for user acceptance and to meet the growing number of regulatory standards. Counterfactual explanations as a rather easy to interpret concept of local explanations com bined with the generative power of Variational Autoencoder (VAE) and their ability to learn distributions of latent representations can offer information to fulfill the needs of machine learning experts and non-expert users at the same time. Most current studies leveraging the power of deep generative models for counterfactual generation focus on vision data. We focus on anomaly detection applications on real world tabular data in the two high-risk fields of financial accounting and healthcare. We give an overview on constructions of counterfactual explanations and a categorization of current approaches to produce counterfactual explanations. We are investigating supervised extensions of the VAE for simultaneous classification and counterfactual generation. Therefor we explore the connection between different approaches of probabilistic modelling and separability properties in latent space. We discuss their applicability to anomaly detection and evaluation criteria.