Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data

TL;DR

FastDCFlow is an efficient method for generating counterfactual explanations in tabular data using normalizing flows and target encoding, addressing computational challenges and categorical variable perturbations.

Contribution

The paper introduces FastDCFlow, a novel approach that improves counterfactual explanations for tabular data by combining normalizing flows with target encoding for categorical variables.

Findings

Outperforms existing methods in multiple metrics

Balances trade-offs effectively in counterfactual explanations

Handles categorical variables with target encoding

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 the 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 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. The source code is available in the following repository: https://github.com/sumugit/FastDCFlow.