<ul><li>Inferring causal relationships between variables is essential for understanding multivariate interactions in complex systems.</li><li>Knowledge-based causal discovery relies on reasoning over metadata of variables, offering an alternative to traditional observational data methods.</li><li>A novel approach integrating Knowledge Graphs with Large Language Models improves knowledge-based causal discovery by identifying informative subgraphs and refining their selection.</li><li>Extensive experiments on biomedical and open-domain datasets show that this method outperforms baselines in inferring causal relationships, offering a significant improvement in F1 scores.</li></ul>

Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery

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