Understanding Counterfactual Generation using Maximum Mean Discrepancy

Wei Zhang, Brian Barr, John Paisley

Abstract: With the dramatic development of deep learning in the past decade, interpretability has been one of the most important challenges that often prevents neural networks from being applied to fields such as finance. Among many existing explainable analyses, counterfactual generation has become widely used for understanding neural networks and making tailored recommendations. However, few studies are devoted to providing quantitative measures for evaluating counterfactuals. In this paper, we propose a quantitative approach based on maximum mean discrepancy (MMD). We employ several existing counterfactual methods to demonstrate this proposed method on the MNIST image data set and two tabular financial data sets, Lending Club (LCD) and Give Me Some Credit (GMC). The results demonstrate the potential usefulness as well as the simplicity of the proposed method.