  Counterfactual explanation is a common class of methods to make local
explanations of machine learning decisions. For a given instance, these methods
aim to find the smallest modification of feature values that changes the
predicted decision made by a machine learning model. One of the challenges of
counterfactual explanation is the efficient generation of realistic
counterfactuals. To address this challenge, we propose VCNet-Variational
Counter Net-a model architecture that combines a predictor and a counterfactual
generator that are jointly trained, for regression or classification tasks.
VCNet is able to both generate predictions, and to generate counterfactual
explanations without having to solve another minimisation problem. Our
contribution is the generation of counterfactuals that are close to the
distribution of the predicted class. This is done by learning a variational
autoencoder conditionally to the output of the predictor in a join-training
fashion. We present an empirical evaluation on tabular datasets and across
several interpretability metrics. The results are competitive with the
state-of-the-art method.
