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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.14856 |
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| _version_ | 1866914479554428928 |
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| author | Allein, Liesbeth Pineda-Castañeda, Nataly Rocci, Andrea Moens, Marie-Francine |
| author_facet | Allein, Liesbeth Pineda-Castañeda, Nataly Rocci, Andrea Moens, Marie-Francine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14856 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ClimateCause: Complex and Implicit Causal Structures in Climate Reports Allein, Liesbeth Pineda-Castañeda, Nataly Rocci, Andrea Moens, Marie-Francine Computation and Language Artificial Intelligence 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. |
| title | ClimateCause: Complex and Implicit Causal Structures in Climate Reports |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.14856 |