Streamlined Document-Level Event Causality Identification with Large Language Models

Document-level event causality identification (DECI) is crucial for deep text understanding, yet traditional methods struggle with error propagation, neglect document structure, and incur high computational costs. This paper introduces Prompt-based Structure-Aware Causal Identification (PSACI), a novel approach leveraging Large Language Models (LLMs) through carefully designed prompts. PSACI implicitly captures document structure and performs causal reasoning by instructing the model to identify causal event pairs and generate rationales, eliminating the need for complex multi-task learning or explicit graph construction. Evaluated on EventStoryLine and Causal-TimeBank datasets, PSACI outperforms state-of-the-art baselines, particularly in cross-sentence causality identification, achieving an F1-score of 53.2% on EventStoryLine and 63.5% on Causal-TimeBank. Human evaluation confirms the high coherence and relevance of generated rationales. Our findings demonstrate the effectiveness of prompt engineering for DECI, offering a streamlined and adaptable framework with enhanced performance and interpretability.