Assessing LLM Reasoning Through Implicit Causal Chain Discovery in Climate Discourse

2025-10-15

AI Summary

Large language models (LLMs) struggle with mechanistic causal reasoning—specifically, identifying implicit causal chains bridging “cause → effect” gaps in polarized climate discourse. Method: We propose the first diagnostic framework for implicit causal chain discovery tailored to climate narratives and introduce a dedicated benchmark dataset. Our evaluation employs nine state-of-the-art LLMs, integrating self-consistency analysis, confidence calibration, and human adjudication across multiple dimensions. Contribution/Results: While generated causal chains exhibit logical coherence and high model confidence, they predominantly rely on statistical association matching rather than genuine causal inference. This reveals a fundamental limitation of current LLMs in understanding underlying causal mechanisms. Our work establishes an extensible, empirically grounded evaluation paradigm for interpretable causal reasoning, offering both methodological innovation and concrete evidence to advance research in trustworthy, mechanism-aware AI for climate communication.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

How does a cause lead to an effect, and which intermediate causal steps explain their connection? This work scrutinizes the mechanistic causal reasoning capabilities of large language models (LLMs) to answer these questions through the task of implicit causal chain discovery. In a diagnostic evaluation framework, we instruct nine LLMs to generate all possible intermediate causal steps linking given cause-effect pairs in causal chain structures. These pairs are drawn from recent resources in argumentation studies featuring polarized discussion on climate change. Our analysis reveals that LLMs vary in the number and granularity of causal steps they produce. Although they are generally self-consistent and confident about the intermediate causal connections in the generated chains, their judgments are mainly driven by associative pattern matching rather than genuine causal reasoning. Nonetheless, human evaluations confirmed the logical coherence and integrity of the generated chains. Our baseline causal chain discovery approach, insights from our diagnostic evaluation, and benchmark dataset with causal chains lay a solid foundation for advancing future work in implicit, mechanistic causal reasoning in argumentation settings.

Problem

• Evaluating LLMs' causal reasoning via implicit chain discovery
• Analyzing intermediate causal steps in climate change arguments
• Assessing mechanistic reasoning versus associative pattern matching

Innovation

• Implicit causal chain discovery for LLM evaluation
• Diagnostic framework with climate argumentation datasets
• Baseline approach for mechanistic causal reasoning