Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models

arXiv:2402.15301v1 Announce Type: cross
Abstract: 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 …

abstract advance arxiv biases causal inference collection cs.cl cs.lg data data collection discovery graph inference knowledge language language models large language large language models llms recovery relations retrieval retrieval-augmented statistical stat.me type variables