Causal Reasoning as a Path to Explainable and Generalizable Artificial Intelligence

Abstract

Artificial Intelligence (AI) systems, particularly those based on deep learning, have achieved extraordinary success in pattern recognition and predictive tasks. However, their reliance on correlation-based learning has raised serious concerns regarding explainability, robustness, fairness, and generalization. These limitations are especially problematic in high-stakes domains such as healthcare, autonomous systems, finance, and governance, where AI decisions must be transparent, reliable, and adaptable to changing environments. Causal reasoning offers a promising paradigm to address these challenges by enabling AI systems to move beyond surface-level correlations toward an understanding of underlying cause–effect relationships.

This paper explores causal reasoning as a foundational pathway to explainable and generalizable artificial intelligence. It examines the theoretical underpinnings of causal inference, contrasts causal and correlational learning, and analyzes how causal models enhance explainability and out-of-distribution generalization. The paper further reviews emerging approaches for integrating causal reasoning into modern AI systems, including structural causal models, counterfactual learning, invariant representations, and hybrid neuro-symbolic architectures. Key applications, challenges, and future research directions are discussed. The study argues that causal reasoning is not merely an auxiliary feature but a necessary component for building trustworthy, human-aligned, and generalizable AI systems.