Related Experiment Video
Updated: Sep 23, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Knowledge augmented causal discovery through large language models and Knowledge Graphs: Application in chronic low
Damon Lin1, Marzieh Mussavi Rizi2, Conor O'Neill3
1Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA, USA.
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.
