FastDCFlow: Fast and Diverse Counterfactual Explanations Using Normalizing Flows

Yuta Sumiya, Hayaru Shouno

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: Counterfactual explanations, Input-based methods, Model-based methods, Tabular data, TargetEncoding, Normalizing Flows, Latent representation, Reparametrization trick, Diversity

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TL;DR: FastDCFlow: Fast and Diverse CEs Using Normalizing Flows

Abstract: Machine-learning models, which are known to accurately predict patterns from large datasets, are crucial in decision-making. Consequently, counterfactual explanations-methods explaining predictions by introducing input perturbations-have become prominent. These perturbations often suggest ways to alter predictions, leading to actionable recommendations. However, the current techniques require resolving the optimization problems for each input change, rendering them computationally expensive. In addition, traditional encoding methods inadequately address the perturbations of categorical variables in tabular data. Thus, this study propose "FastDCFlow," an efficient counterfactual explanation method using normalizing flows. The proposed method captures complex data distributions, learns meaningful latent spaces that retain proximity, and improves the predictions. For categorical variables, we employed "TargetEncoding," which respects ordinal relationships and includes perturbation costs. The proposed method outperformed existing methods in multiple metrics, striking a balance between trade-offs for counterfactual explanations.

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Submission Number: 2663