Causal Reasoning Models for Artificial General Intelligence: Foundations, Architectures, and Scalable Learning Frameworks

Abstract

Artificial General Intelligence (AGI) requires systems that can move beyond pattern recognition to understand cause-and-effect relationships in complex environments. Causal reasoning is a fundamental component of human intelligence, enabling prediction, explanation, and decision-making under uncertainty. However, most current AI systems rely heavily on statistical correlations rather than true causal understanding. This paper explores causal reasoning models and their integration into AGI frameworks. It examines structural causal models, probabilistic reasoning, and hybrid learning architectures. A unified causal cognitive architecture is proposed to enable reasoning, intervention, and counterfactual analysis. The paper also discusses implementation challenges, evaluation metrics, and future research directions for developing robust and explainable AGI systems.

KEYWORDS: Artificial General Intelligence, Causal Reasoning, Structural Causal Models, Counterfactual Learning, Explainable AI, Machine Intelligence