Discovery of Maximally Consistent Causal Orders with Large Language Models

• URL: http://arxiv.org/abs/2412.14019v2
• Date: Sun, 09 Feb 2025 16:40:38 GMT
• Title: Discovery of Maximally Consistent Causal Orders with Large Language Models
• Authors: Federico Baldo, Simon Ferreira, Charles K. Assaad,
• Abstract summary: Causal discovery is essential for understanding complex systems. Traditional methods often rely on strong, untestable assumptions. We propose a novel method to derive a class of acyclic tournaments.
• Score: 0.8192907805418583
• License:
• Abstract: Causal discovery is essential for understanding complex systems, as it aims to uncover causal relationships from observational data in the form of a causal directed acyclic graph (DAG). However, traditional methods often rely on strong, untestable assumptions, which makes them unreliable in real applications. Large Language Models (LLMs) present a promising alternative for extracting causal knowledge from text-based metadata, which consolidates domain expertise. However, LLMs are prone to unreliability and hallucinations, necessitating strategies that account for their limitations. One such strategy involves leveraging a consistency measure to evaluate reliability. Additionally, most text metadata does not clearly distinguish direct causal relationships from indirect ones, further complicating the discovery of a causal DAG. As a result, focusing on causal orderings, rather than causal DAGs, emerges as a more practical and robust approach. We propose a novel method to derive a class of acyclic tournaments (representing plausible causal orders) that maximizes a consistency score derived from an LLM. Our approach begins by computing pairwise consistency scores between variables, yielding a semi-complete directed graph that aggregates these scores. From this structure, we identify optimal acyclic tournaments, prioritizing those that maximize consistency across all configurations. We tested our method on both well-established benchmarks, as well as real-world datasets from epidemiology and public health. Our results demonstrate the effectiveness of our approach in recovering a class of causal orders.

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