Explainable AI (XAI) via Causal Inference and Counterfactual Analysis

Abstract

Current Explainable AI (XAI) methods frequently deliver post-hoc explanations that lack a fundamental understanding of the causal relationships underpinning AI decision-making. This paper proposes a novel approach to XAI that integrates causal inference and counterfactual analysis, aiming to generate more insightful and actionable explanations. Our methodology leverages causal discovery techniques to identify the key causal factors driving a model's decisions, moving beyond merely highlighting correlations. Furthermore, we employ counterfactual analysis to simulate "what-if" scenarios, allowing us to assess the potential impact of altering specific input features and understand the sensitivity of the model. This approach provides a deeper understanding of the AI system's behavior, ultimately leading to more robust and trustworthy AI models. We demonstrate the potential of this integrated framework through conceptual arguments and a detailed outline of the proposed methodology.