Generating Efficiently Realistic Counterfactual Explanations

Abstract

This article introduces VCNet –Variational CounterNet—a method for generating realistic counterfactuals, and its extension ImmutableVCNet . VCNet aims to generate counterfactuals that are representative of their predicted classes in the context of tabular data. Moreover, it aims to overcome the limitations related to the post-hoc character and optimization procedure of the state-of-the-art approaches, by reducing the computing time of the counterfactual generation and reaching high levels of validity. However, state-of-the-art methods that succeed in overcoming the aforementioned limitations suffer from a lack of realism with regard to the counterfactuals generated. VCNet addresses this concern by adding realism constraints to the counterfactual generation process. Our approach is based on a conditional variational autoencoder (cVAE) to model the distributions for every class at once. Thus, generated counterfactuals not only belong to the data distribution but belong to the data distribution of a given class. The ImmutableVCNet extends VCNet to overcome the limitation of handling immutable features. We conducted several ablation studies to investigate the impact of the different choices within the VCNet architecture. Furthermore, we conducted empirical studies that demonstrate the significant benefits of our approach in generating realistic counterfactuals. We evaluate VCNet against ImmutableVCNet and also ImmutableVCNet against a variety of state-of-the-art counterfactual methods.