# ClimateCause: Complex and Implicit Causal Structures in Climate Reports

arXiv: https://arxiv.org/abs/2604.14856 · HTML: https://mlsanity.com/paper/2604.14856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [Measuring what Matters: Construct Validity in Large Language Model Benchmarks](https://mlsanity.com/paper/2511.04703/) (2025)
- [Guided Generation of Cause and Effect](https://mlsanity.com/paper/2107.09846/) (2021)
- [CRAB: Assessing the Strength of Causal Relationships Between Real-world Events](https://mlsanity.com/paper/2311.04284/) (2023)
- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning](https://mlsanity.com/paper/2201.03647/) (2022)
- [CausalEval: Towards Better Causal Reasoning in Language Models](https://mlsanity.com/paper/2410.16676/) (2024)
- [CausalGraph2LLM: Evaluating LLMs for Causal Queries](https://mlsanity.com/paper/2410.15939/) (2024)
- [Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery](https://mlsanity.com/paper/2412.13667/) (2024)
- [VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos](https://mlsanity.com/paper/2506.02448/) (2025)
- [ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning](https://mlsanity.com/paper/2502.08148/) (2025)
- [Investigating Counterclaims in Causality Extraction from Text](https://mlsanity.com/paper/2510.08224/) (2025)
- [Assessing LLM Reasoning Through Implicit Causal Chain Discovery in Climate Discourse](https://mlsanity.com/paper/2510.13417/) (2025)

## Built on by (most cited first, 1 of 1)

- [Implicit Causal Graph Construction in Text via Chain Discovery](https://mlsanity.com/paper/2606.07525/) (2026)

## Abstract (arXiv)

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.