Integrating Causal Inference into Reinforcement Learning via Large Language Model Reasoning for Transparent and Robust Counterfactual Decision Analytics
DOI:
https://doi.org/10.66280/cis.v1i1.154Abstract
The convergence of reinforcement learning and causal inference represents a fundamental shift in the development of autonomous decision-making systems. Traditional reinforcement learning architectures often struggle with sample efficiency and lack the capacity for structural explanation, frequently failing when faced with out-of-distribution environmental shifts. This research paper investigates the integration of causal inference mechanisms into reinforcement learning frameworks by leveraging the semantic reasoning and world-knowledge capabilities of large language models. By utilizing these models as reasoning engines capable of generating and validating causal graphs, the proposed architecture facilitates transparent and robust counterfactual decision analytics. The integration allows for the transition from purely associative learning to structural understanding, enabling systems to simulate "what-if" scenarios without direct environmental interaction. We explore the system-level implications of this synthesis, focusing on the trade-offs between computational overhead and decision-making robustness. The discussion encompasses the infrastructure required for such large-scale deployment, the governance of automated reasoning, and the socio-technical impacts on fairness and policy. Ultimately, this work argues that the synergy between causal structures and linguistic reasoning provides a pathway toward more interpretable, ethical, and sustainable artificial intelligence in complex socio-technical infrastructures.
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