Knowledge augmented causal discovery through large language models and Knowledge Graphs: Application in chronic low back pain.

OBJECTIVE: Causal discovery from observational data is limited by structural constraints of available datasets, the absence of causal logic, and the lack of external domain knowledge. We propose a Knowledge-Augmented Causal Discovery (KACD) framework that combines data-driven structural learning with LLM-based knowledge systems to overcome these limitations, with application to chronic low back pain (cLBP). METHODS: We evaluated KACD in three configurations of increasing complexity: a vanilla LLM, an LLM augmented with retrieval-augmented generation (RAG), and an LLM augmented with a Knowledge Graph-based RAG system (KG-RAG). Each configuration was benchmarked against a clinically derived, expert-validated causal graph for cLBP. Causal querying employed a decomposed two-phase prompting strategy - separately querying plausibility, statistical association, and temporal precedence - inspired by the methodology used by domain experts during ground truth construction. Performance was evaluated using F1-score, true positive rate, false discovery rate, and structural Hamming distance. RESULTS: KG-RAG with plausibility-based prompting achieved the highest F1 score (0.697), outperforming standard RAG (F1 0.689), vanilla LLM (F1 0.649), and data-driven structural learning alone (F1 0.396), however this was backbone-dependent with RAG outperforming KG-RAG depending on the LLM used. Decomposed causal prompting outperformed direct causal queries across all knowledge system configurations. Combining the data-driven proto-model with any knowledge system consistently raised the performance floor of either method individually across all prompting strategies and system configurations. CONCLUSION: LLM-driven knowledge systems can effectively augment data-driven causal discovery for biomedical causal modeling, with KG-RAG providing superior performance through cross-document synthesis and entity-centered retrieval. These findings support KACD's potential as a decision-support tool for researchers constructing causal models in complex, multi-disciplinary health domains, pending further validation of its use in practice.