Integrating Causal Inference into Reinforcement Learning Pipelines for Robust Counterfactual Reasoning in Generative Large Language Models - Raymond Norwood; Derek Clarke (Computational Intelligence Systems, 2026)
The rapid evolution of generative large language models has fundamentally transformed the landscape of artificial intelligence, yet these systems continue to struggle with high-stakes reasoning tasks that require an understanding of cause-and-effect relationships rather than mere statistical correlations. Current paradigms, which rely heavily on reinforcement learning from human feedback, often fail to instill a true counterfactual understanding in these models, leading to hallucinations or logically inconsistent outputs when faced with "what-if" scenarios. This paper proposes a comprehensive architectural framework for integrating causal inference directly into reinforcement learning pipelines. By embedding structural causal models within the reward mechanism and policy optimization phases, we enable generative agents to simulate and evaluate counterfactual outcomes with greater precision. Our discussion focuses on the systemic implications of this integration, exploring how causal grounding enhances the robustness and reliability of large-scale AI deployments. We examine the structural trade-offs involved in moving beyond associative learning, the infrastructure requirements for causal discovery at scale, and the broader socio-technical impacts on governance, fairness, and automated decision-making. Through detailed conceptual analysis, we argue that the transition from pattern-matching to causal reasoning is a necessary step for the deployment of AI in critical infrastructures such as healthcare, finance, and legal adjudication. The paper concludes by outlining a roadmap for sustainable and ethically grounded causal AI development.