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arxiv: 2604.14856 · v1 · submitted 2026-04-16 · 💻 cs.CL · cs.AI

ClimateCause: Complex and Implicit Causal Structures in Climate Reports

Liesbeth Allein , Nataly Pineda-Casta\~neda , Andrea Rocci , Marie-Francine Moens This is my paper

Pith reviewed 2026-05-10 11:15 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords climate reportscausal structuresimplicit causalitynested causalitycausal discoverylarge language modelscausal reasoningreadability
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The pith

ClimateCause dataset annotates complex implicit causal structures in climate reports and shows LLMs struggle more with chain reasoning than correlations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ClimateCause as a manually annotated dataset drawn from science-for-policy climate reports that captures higher-order causal structures including implicit and nested relations. Cause-effect expressions are normalized into individual relations and labeled for correlation, relation type, and spatiotemporal context to support graph construction. The dataset is applied to quantify statement readability according to the semantic complexity of the underlying causal graphs. Benchmarking of large language models on correlation inference versus causal chain reasoning tasks identifies the latter as a particular difficulty. A reader would care because grasping climate change depends on navigating these intricate causal networks rather than isolated direct links.

Core claim

ClimateCause is created through expert annotation of higher-order causal structures from climate reports, with cause-effect expressions normalized and disentangled into individual relations annotated for correlation, type, and context. This enables construction of causal graphs that include implicit and nested elements. The resource supports readability measurement based on causal graph complexity and reveals through LLM benchmarking that causal chain reasoning poses a greater challenge than correlation inference.

What carries the argument

The ClimateCause dataset of expert-annotated higher-order causal structures from climate reports, including implicit and nested causality, with normalized cause-effect expressions labeled for correlation, relation type, and spatiotemporal context to enable graph-based analysis.

If this is right

  • The dataset enables more rigorous evaluation of models on complex causality beyond explicit direct relations.
  • Readability of climate statements can be quantified using the semantic complexity of their underlying causal graphs.
  • Causal discovery methods can incorporate annotations for correlation, type, and context to build richer graphs from policy documents.
  • Targeted improvements in language models can focus on multi-step causal chain reasoning for domain-specific texts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar annotation schemes for implicit causality could be applied to reports in other policy domains to test model limits on nested reasoning.
  • Integrating causal graph complexity metrics into text analysis tools might aid efforts to make climate information more accessible.
  • Model training that emphasizes chain reasoning on annotated graphs like these could address gaps in handling real-world causal networks.

Load-bearing premise

Expert annotators can consistently and accurately identify and label implicit, nested, and higher-order causal structures in the source climate reports to create reliable ground truth.

What would settle it

A study finding low agreement among multiple experts annotating the same climate report passages for these complex causal relations, or an LLM achieving comparable performance on causal chain reasoning tasks to correlation inference without using ClimateCause-style data.

Figures

Figures reproduced from arXiv: 2604.14856 by Andrea Rocci, Liesbeth Allein, Marie-Francine Moens, Nataly Pineda-Casta\~neda.

Figure 1
Figure 1. Figure 1: A sample from the ClimateCause dataset, showcasing the complex causal graphs and fine-grained annotations it contains. Mostafazadeh et al., 2016; Dunietz et al., 2017; Romanou et al., 2023; Tan et al., 2022; Vo et al., 2025; Pineda and Allein, 2025a,b). They primar￾ily capture explicit direct cause-effect relations and omit those that are implicitly reported through word and sentence semantics; e.g., “anth… view at source ↗
Figure 2
Figure 2. Figure 2: Mean readability scores of statements in Po [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces 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 relations for graph construction, with annotations for correlation, relation type, and spatiotemporal context. The work shows the dataset's utility for quantifying readability via causal graph complexity and benchmarks LLMs on correlation inference versus causal chain reasoning, identifying the latter as a key challenge.

Significance. If the annotations are reliable, ClimateCause could address a gap in causal discovery datasets by targeting complex, implicit structures in climate policy texts. The readability metric and LLM benchmarking provide concrete demonstrations of the dataset's potential value, particularly in exposing model weaknesses on chain reasoning that could inform targeted improvements in scientific NLP.

major comments (1)
  1. [Dataset Construction / Methods] The validity of the dataset, readability quantification, and all LLM benchmarking results rests on the expert annotations of implicit, nested, and higher-order causal structures. No inter-annotator agreement statistics, number of annotators, adjudication protocol, or disagreement resolution details are provided (see dataset construction description in the abstract and implied methods). For this class of subjective structures, low agreement would mean performance gaps cannot be confidently attributed to model limitations rather than label noise.
minor comments (1)
  1. [Abstract] The abstract refers to 'unique annotations for cause-effect correlation, relation type, and spatiotemporal context' without specifying the exact label inventory, annotation guidelines, or examples of how nested relations are represented in the graphs.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights a key aspect of dataset reliability that requires clarification. We address the major comment point by point below.

read point-by-point responses

  1. Referee: [Dataset Construction / Methods] The validity of the dataset, readability quantification, and all LLM benchmarking results rests on the expert annotations of implicit, nested, and higher-order causal structures. No inter-annotator agreement statistics, number of annotators, adjudication protocol, or disagreement resolution details are provided (see dataset construction description in the abstract and implied methods). For this class of subjective structures, low agreement would mean performance gaps cannot be confidently attributed to model limitations rather than label noise.

    Authors: We agree that the absence of these details in the current manuscript is a limitation, as they are essential for validating annotations of complex, implicit causal structures. The manuscript describes the dataset as 'manually expert-annotated' but does not provide the requested statistics or protocols. In the revised version, we will expand the Methods section (and update the abstract if needed) to include the number of annotators, inter-annotator agreement metrics, and the full adjudication protocol for resolving disagreements. This will allow readers to assess whether performance gaps in LLM benchmarking can be confidently attributed to model limitations. revision: yes

Circularity Check

0 steps flagged

No circularity; dataset introduction with independent empirical evaluation

full rationale

The paper introduces ClimateCause as a new expert-annotated dataset of causal structures from climate reports and demonstrates its use via readability metrics and LLM benchmarking. No mathematical derivations, parameter fitting, self-referential predictions, or load-bearing self-citations appear in the provided text. All claims rest on newly created annotations and direct empirical comparisons to external LLM performance, satisfying the self-contained criterion with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central contribution rests on the assumption that expert annotations faithfully capture the intended causal structures; no free parameters or new invented entities are introduced.

axioms (1)
  • domain assumption Expert manual annotations can be performed consistently and accurately enough to serve as reliable ground truth for implicit, nested, and higher-order causal structures in the source reports.
    The entire dataset and all downstream claims depend on this premise.

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Reference graph

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