CUBE: Causal Intervention-based Counterfactual Explanation for Prediction Models (Extended Abstract)

With the explosion of artificial intelligence in various fields, providing causal explanations for predictive models becomes urgent. In this study, we investigate causal counterfactual explanation generation and propose CUBE, a causal intervention-based counterfactual explanation method. This work models the counterfactual generation process as a causal intervention model for the first time and employs a causal director to integrate causal relationships. Furthermore, this work proposes a model-based framework to improve counterfactual generation efficiency. The experimental results validate that CUBE outperforms baselines in terms of both lower time costs and higher explanation quality.