Large language models (LLMs), while capable of generating plausible diagnostic plans from sensor data, inherently lack true causal reasoning capabilities and are prone to hallucinations. To address this limitation, we propose a hybrid AI framework that integrates LLMs with structured causal inference to enable robust, interpretable decision-making in predictive health monitoring (PHM) for complex systems. Our architecture positions the LLM as a planning agent that infers candidate failure modes and troubleshooting steps, while delegating causal evaluation to an external inference model grounded in formal causal principles. The system constructs a localized causal knowledge graph (KG) by retrieving top-k similar historical traces based on the current sensor context, and uses this graph to simulate and evaluate the impact of potential actions. We explore three strategies for handling multi-step diagnostic plans: step-wise decomposition, compound treatment modelling, and sequential intervention chains. Recommendations are ranked based on their estimated effect on resolution likelihood and further validated by a dedicated Evaluator Agent using counterfactual reasoning. Our results demonstrate that augmenting LLM-generated plans with external causal inference significantly improves relevance, consistency, and safety—offering a deployable blueprint for high-stakes PHM scenarios where LLMs alone cannot be trusted to reason reliably.
Rajarajan Kirubanandan (Sun,) studied this question.