Efficient Causal Graph Discovery Using Large Language Models

📰 ArXiv cs.AI

Efficient causal graph discovery using large language models with a breadth-first search approach

advanced Published 6 Apr 2026

Action Steps

Utilize large language models for causal graph discovery
Implement a breadth-first search approach to reduce the number of queries
Compare the proposed method with existing pairwise query approaches to evaluate efficiency gains
Apply the framework to real-world datasets to demonstrate its effectiveness

Who Needs to Know This

Data scientists and AI engineers on a team can benefit from this framework as it enables efficient causal graph discovery, which is crucial for understanding complex relationships in data. This can be particularly useful in applications such as predictive modeling and decision-making.

Key Insight

💡 The proposed framework reduces the number of queries required for causal graph discovery from quadratic to linear, making it more practical for larger graphs

Key Takeaways

Efficient causal graph discovery using large language models with a breadth-first search approach

Full Article

Title: Efficient Causal Graph Discovery Using Large Language Models

Abstract:
arXiv:2402.01207v5 Announce Type: replace-cross Abstract: We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily in

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