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ClimateCause: Complex and Implicit Causal Structures in Climate Reports

Conference: ACL 2026 arXiv: 2604.14856 Code: GitHub Area: Causal Inference / Dataset Keywords: Causal Discovery, Climate Change, Implicit Causality, Nested Causality, IPCC Report

TL;DR

ClimateCause constructs the first expert-annotated dataset for complex and implicit causal structures in climate reports (874 causal relations), supporting nested causality, multi-event decomposition, correlation direction, and spatiotemporal context annotation. LLM benchmarking shows causal chain reasoning remains a major challenge.

Method

Key Designs

  1. Noun Phrase Reconstruction and Multi-Event Decomposition: Standardizes causes/effects into comparable canonical forms, with Belongs_to and Combined fields distinguishing exemplification from joint action.

  2. Implicit and Nested Causality Annotation: Captures causality expressed through semantics rather than explicit triggers (e.g., "anthropogenic greenhouse gas emissions" implicitly contains humans → greenhouse gas emissions).

  3. Causal Graph Semantic Complexity Readability Metric: Five complexity dimensions with min-max normalization, measuring the cognitive complexity of causal reasoning rather than surface-level readability.

Key Experimental Results

  • 57.33% of statements contain semantically complex causal structures
  • LLMs perform far worse on causal chain reasoning than correlation inference
  • Statement length significantly correlates with causal complexity (\(r=0.590\))

Highlights & Insights

  • Causal structure readability metric is novel and practically valuable — helps assess report comprehensibility for policymakers
  • Nested causality concept transferable to other specialized domains (medical reports, legal documents)

Rating

  • Novelty: ⭐⭐⭐⭐
  • Experimental Thoroughness: ⭐⭐⭐
  • Writing Quality: ⭐⭐⭐⭐
  • Value: ⭐⭐⭐⭐