CounterNet: End-to-End Training of Counterfactual Aware Predictions

This work presents CounterNet, a novel end-to-end learning framework which integrates the predictive model training and counterfactual (CF) explanation generation into a single end-to-end pipeline. Counterfactual explanations attempt to find the smallest modification to the feature values of an instance that changes the prediction of the ML model to a predefined output. Prior CF explanation techniques rely on solving separate time-intensive optimization problems for every single input instance to find CF examples, and also suffer from the misalignment of objectives between model predictions and explanations, which leads to significant shortcomings in the quality of CF explanations. CounterNet, on the other hand, integrates both prediction and explanation in the same framework, which enables the optimization of the CF example generation only once together with the predictive model. We propose a novel variant of back-propagation which can help in effectively training CounterNet's network. Finally, we conduct extensive experiments on multiple real-world datasets. Our results show that CounterNet generates high-quality predictions, and corresponding CF examples (with high validity) for any new input instance significantly faster than existing state-of-the-art baselines.