LLMs enhance causal discovery with new argumentation framework

Researchers have developed a novel approach to causal discovery by integrating large language models (LLMs) with the Causal Assumption-based Argumentation (ABA) framework. This method leverages LLMs as imperfect experts to extract semantic structural priors from variable names and descriptions, combining this with conditional-independence evidence. Experiments on standard benchmarks and synthetic graphs show state-of-the-art performance, and a new evaluation protocol has been introduced to address memorization bias in LLM assessments for causal discovery. AI

IMPACT This research could improve the accuracy and interpretability of causal inference, impacting fields that rely on understanding cause-and-effect relationships.

RANK_REASON This is a research paper detailing a new methodology for causal discovery using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Causal Assumption-based Argumentation
• causal discovery
• Fabrizio Russo
• Hugging Face
• large-language models

• paper
• model release

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