# Challenges and Opportunities in Causality Analysis Using Large Language Models

## TL;DR

This paper explores how large language models can extract causal relationships from text, highlighting their potential despite limitations like hallucinations.

## Contribution

The paper introduces new insights into how LLMs can perform causality analysis using diverse perspectives like counterfactual and Aristotelian frameworks.

## Key findings

- GPT and Gemini models achieve 88–91% agreement on causal relationships, surpassing typical inter-annotator agreement.
- LLMs can explain and critique causal analyses, applying multiple causal perspectives effectively.
- LLMs may help overcome annotated data sparsity and shift focus from annotations to semantic understanding of causality.

## Abstract

This article examines the challenges and opportunities in extracting causal information from text with Large Language Models (LLMs). It first establishes the importance of causality extraction and then explores different views on causality, including common sense ideas informing different data annotation schemes, Aristotle’s Four Causes, and Pearl’s Ladder of Causation. The paper notes the relevance of this conceptual variety for the task. The text reviews datasets and work related to finding causal expressions, both using traditional machine learning methods and LLMs. Although the known limitations of LLMs—hallucinations and lack of common sense—affect the reliability of causal findings, GPT and Gemini models (GPT-5 and Gemini 2.5 Pro and others) show the ability to conduct causality analysis; moreover, they can even apply different perspectives, such as counterfactual and Aristotelian. They are also capable of explaining and critiquing causal analyses: we report an experiment showing that in addition to largely flawless analyses, the newer models exhibit very high agreement of 88–91% on causal relationships between events—much higher than the typically reported inter-annotator agreement of 30–70%. The article concludes with a discussion of the lessons learned about these challenges and questions how LLMs might help address them in the future. For example, LLMs should help address the sparsity of annotated data. Moreover, LLMs point to a future where causality analysis in texts focuses not on annotations but on understanding, as causality is about semantics and not word spans. The Appendices and shared data show examples of LLM outputs on tasks involving causal reasoning and causal information extraction, demonstrating the models’ current abilities and limits.