ClimateCause: Complex and Implicit Causal Structures in Climate Reports
- Publication type
- U
- Publication status
- Published
- Authors
- Allein, L, Pineda-Castañeda, N., Rocci, A., & Moens, M.
- Series
- Findings of the Association for Computational Linguistics: ACL 2026
- Pagination
- 25458-25488
- Publisher
- Association for Computational Linguistics (Stroudsburg, PA, USA)
- Conference
- The 64th Annual Meeting of the Association for Computational Linguistics (San Diego, California, USA)
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- (.pdf)
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Abstract
Understanding climate change requires reasoning over complex causal networks. Yet, existing causal discovery datasets predominantly capture explicit, direct causal relations. We introduce ClimateCause, a manually expert-annotated dataset of higher-order causal structures from science-for-policy climate reports, including implicit and nested causality. Cause-effect expressions are normalized and disentangled into individual causal relations to facilitate graph construction, with unique annotations for cause-effect correlation, relation type, and spatiotemporal context. We further demonstrate ClimateCause’s value for quantifying readability based on the semantic complexity of causal graphs underlying a statement. Finally, large language model benchmarking on correlation inference and causal chain reasoning highlights the latter as a key challenge.