Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data
This addresses the problem of inefficient and inadequate counterfactual explanations for decision-making systems, representing an incremental improvement.
The paper tackles the computational expense and categorical variable handling in counterfactual explanations for tabular data by proposing FastDCFlow, which uses normalizing flows and TargetEncoding to outperform existing methods in multiple metrics.
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.