ClimateCause: Complex and Implicit Causal Structures in Climate Reports

• URL: http://arxiv.org/abs/2604.14856v1
• Date: Thu, 16 Apr 2026 10:43:29 GMT
• Title: ClimateCause: Complex and Implicit Causal Structures in Climate Reports
• Abstract summary: Cause-effect expressions are normalized and disentangled into individual causal relations.<n>A large language benchmarking model on correlation inference and causal chain reasoning highlights the latter as a key challenge.
• Score: 22.71538103040959
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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