Class-Aware Reinforcement Learning for Counterfactual Explanation Generation

2026-07-30

AI Summary

This work proposes a class-aware reinforcement learning framework to enhance the efficiency and quality of counterfactual explanation generation. By incorporating the model’s predicted class information into the state representation—a novel design in this domain—the proposed approach guides the policy to more effectively explore counterfactual instances that satisfy validity, sparsity, and proximity constraints. The integration of class awareness significantly accelerates policy convergence and improves reward optimization. Empirical evaluation across seven diverse datasets demonstrates that the method generates a greater number of high-quality explanations with fewer training episodes compared to existing approaches. Furthermore, feature importance analyses using SHAP and LIME confirm that class information plays a critical role in guiding action selection during the generation process.

Abstract

Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome. Reinforcement learning (RL) offers a promising approach for CFE generation, enabling efficient exploration of counterfactual instances while ensuring control over key metrics like validity, sparsity, and proximity. Previous studies have formulated RL states exclusively using features derived from the predictors in the supervised dataset. This study explores the impact of including an instance's predicted class, alongside features derived from the predictors, in the RL state representation for generating CFEs. The hypothesis is that class-awareness enhances exploration efficiency and improves policy optimality. We compare the proposed class-aware RL method with the class-blind RL method, which is similar but excludes the instance's class information from the state representation. The comparison was conducted using seven datasets from diverse domains, varying in size. The results show that during training, class-aware RL offers benefits in terms of convergence speed, reward optimization, and episode length reduction. Moreover, it generates significantly more valid CFEs compared to class-blind RL. Finally, the instance's class-based feature consistently ranks among the most influential predictors in RL's action-selection, as shown by the SHAP and LIME values, underscoring the significance of class-awareness in RL for CFE generation. The impact is heightened clarity, faster learning, improved validity, and more effective counterfactual generation across diverse datasets.

Problem

• Counterfactual Explanation
• Reinforcement Learning
• Class-Awareness
• Interpretability
• State Representation

Innovation

• class-aware reinforcement learning
• counterfactual explanation
• interpretability
• state representation
• reinforcement learning