  Cognitive science and symbolic AI research suggest that event causality
provides vital information for story understanding. However, machine learning
systems for story understanding rarely employ event causality, partially due to
the lack of methods that reliably identify open-world causal event relations.
Leveraging recent progress in large language models, we present the first
method for event causality identification that leads to material improvements
in computational story understanding. Our technique sets a new state of the art
on the COPES dataset (Wang et al., 2023) for causal event relation
identification. Further, in the downstream story quality evaluation task, the
identified causal relations lead to 3.6-16.6% relative improvement on
correlation with human ratings. In the multimodal story video-text alignment
task, we attain 4.1-10.9% increase on Clip Accuracy and 4.2-13.5% increase on
Sentence IoU. The findings indicate substantial untapped potential for event
causality in computational story understanding. The codebase is at
https://github.com/insundaycathy/Event-Causality-Extraction.
