Graph-Based Causal Inference for Explainable AI

Abstract

Explainable AI (XAI) is a rapidly growing field focused on making AI models more transparent and understandable to humans. However, many existing XAI techniques primarily focus on post-hoc explanations, often without a deep understanding of the underlying causal relationships driving the model's decisions. This paper proposes a novel approach leveraging graph-based causal inference to generate more robust and meaningful explanations for AI models. The core idea is to construct a causal graph that explicitly represents the relationships between input features and model outputs. This graph allows us to identify the key causal pathways responsible for the model's predictions, providing a transparent and actionable explanation. We demonstrate the feasibility and potential benefits of this approach, arguing that incorporating causal understanding into XAI will lead to more reliable and interpretable AI systems. The generated explanations are grounded in causal relationships rather than simply highlighting correlations, addressing a significant limitation of current XAI methods. We outline the methodology, discuss potential challenges, and highlight the advantages of this graph-based causal inference framework for enhancing the explainability of AI models.