When Causal Discovery Meets LLMs

Music City Center 

Traditional methods for causal graph recovery often rely on statistical estimation or expert input, which can be limited by bias and incomplete knowledge. In this presentation, we introduce an approach that integrates large language models (LLMs) with constraint-based causal discovery to infer causal structures from scientific literature. Our method employs LLMs as knowledge extractors to identify associational relationships among variables from extensive scientific corpora. These relationships are then refined into causal graphs via constraint-based algorithms that eliminate inconsistent connections.

Rather than depending on LLMs for complex causal reasoning, our method leverages their strength in interpreting and extracting information from large-scale scientific texts. This allows us to uncover nuanced associational and causal insights without relying solely on the models' reasoning capabilities. By integrating textual knowledge extraction with causal inference techniques, our method provides a scalable, automated solution for causal discovery, mitigating human bias and harnessing the collective knowledge embedded in scientific discourse.

LLMs

Causal discovery and recovery