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Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
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Causal graph recovery is traditionally done using statistical estimation-based methods or based on individual's knowledge about variables of interests. They often suffer from data collection biases and limitations of individuals' knowledge. The advance of large language models (LLMs) provides opportunities to address these problems. We propose a novel method that leverages LLMs to deduce causal relationships in general causal graph recovery tasks. This method leverages knowledge compressed in LLMs and knowledge LLMs extracted from scientific publication database as well as experiment data about factors of interest to achieve this goal. Our method gives a prompting strategy to extract associational relationships among those factors and a mechanism to perform causality verification for these associations. Comparing to other LLM-based methods that directly instruct LLMs to do the highly complex causal reasoning, our method shows clear advantage on causal graph quality on benchmark datasets. More importantly, as causality among some factors may change as new research results emerge, our method show sensitivity to new evidence in the literature and can provide useful information for updating causal graphs accordingly.
Forward citations
Cited by 6 Pith papers
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LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery
LLMs are unreliable causal reasoners, so they should be limited to non-decisional search support in causal discovery algorithms.
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Retrieval Augmented Generation based Large Language Models for Causality Mining
Dynamic retrieval of few-shot examples by causal connective and semantic similarity improves LLM causality detection and extraction over static prompting in most tested settings.
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Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
A learning-adapting-acting framework that uses an LLM to build and update a causal graph of the game world, then uses that graph to generate goals and shape rewards for an RL agent in Crafter.
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