Causal Graph Discovery with Retrieval-Augmented Generation based Large
  Language Models
Yuzhe Zhang Yipeng Zhang Yidong Gan Lina Yao Chen Wang
Causal graph recovery is essential in the field of causal inference.
Traditional methods are typically knowledge-based or statistical
estimation-based, which are limited by data collection biases and individuals'
knowledge about factors affecting the relations between variables of interests.
The advance of large language models (LLMs) provides opportunities to address
these problems. We propose a novel method that utilizes the extensive knowledge
contained within a large corpus of scientific literature to deduce causal
relationships in general causal graph recovery tasks. This method leverages
Retrieval Augmented-Generation (RAG) based LLMs to systematically analyze and
extract pertinent information from a comprehensive collection of research
papers. Our method first retrieves relevant text chunks from the aggregated
literature. Then, the LLM is tasked with identifying and labelling potential
associations between factors. Finally, we give a method to aggregate the
associational relationships to build a causal graph. We demonstrate our method
is able to construct high quality causal graphs on the well-known SACHS dataset
solely from literature.