Counterfactual Visual Explanation via Causally-Guided Adversarial Steering

2025-07-13

AI Summary

Existing counterfactual visual explanation methods neglect causal mechanisms in image generation, rendering them vulnerable to spurious correlations—leading to redundant perturbations and unreliable explanations. To address this, we propose CECAS, the first framework to systematically integrate causal modeling into counterfactual image generation. CECAS constructs a causal graph to identify genuine causal features and employs gradient-guided adversarial fine-tuning to precisely localize and minimize perturbations within critical regions. This approach effectively disentangles true causal features from spurious associations. Evaluated across multiple benchmark datasets, CECAS significantly improves explanation effectiveness, sparsity, proximity, and fidelity—achieving a superior balance among all four metrics. Our core contribution is establishing a causally grounded counterfactual generation paradigm, providing a theoretically rigorous and empirically effective solution for interpretable AI.

Technology Category

• Machine Learning: Causal Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent work on counterfactual visual explanations has contributed to making artificial intelligence models more explainable by providing visual perturbation to flip the prediction. However, these approaches neglect the causal relationships and the spurious correlations behind the image generation process, which often leads to unintended alterations in the counterfactual images and renders the explanations with limited quality. To address this challenge, we introduce a novel framework CECAS, which first leverages a causally-guided adversarial method to generate counterfactual explanations. It innovatively integrates a causal perspective to avoid unwanted perturbations on spurious factors in the counterfactuals. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches across multiple benchmark datasets and ultimately achieves a balanced trade-off among various aspects of validity, sparsity, proximity, and realism.

Problem

• Generates counterfactual visual explanations with causal guidance
• Avoids spurious correlations in image generation process
• Balances validity, sparsity, proximity, and realism in explanations

Innovation

• Causally-guided adversarial method for explanations
• Integrates causal perspective to avoid spurious perturbations
• Balances validity, sparsity, proximity, and realism