Abstract: Event causality identification (ECI) aims to detect causal relationships between events in textual contexts. Compared to sentence-level ECI which focuses on single sentences, document-level ECI (DECI) needs to process more complex contexts such as long texts and cross-sentence events. Current document-level ECI approaches face two key challenges: 1) learning high-quality context-aware event representation to capture event-specific causal semantic and 2) effectively integrating structural and semantic information for causal reasoning. To address these challenges, we propose MAIGO (multi-granularity context-aware and iterative graph optimization), a novel DECI framework that achieves context-aware event representation through multi-granularity attention mechanisms and dynamically optimizes the event causality graph structure via iterative embedding refinement. MAIGO enhances event embeddings through sentence-level, document-level, and graph-level attention, capturing event-event, event-sentence, and sentence-sentence interactions to produce better representation for events. The framework further constructs an event causality graph (ECG) and jointly optimizes both the ECG structure and event embeddings via masked attention in an iterative optimization process. Extensive experiments and analysis on two benchmark datasets and a multilingual dataset demonstrate MAIGO’s effectiveness, achieving performance gains of 4.1% (causality existence identification) and 7.3% (causality direction identification) in inter-sentence ECI over state-of-the-art models. Further analysis confirms the advantages of the multi-granularity context-aware enhanced representation strategy and iterative graph optimization mechanism.