Benchmarking LLM Causal Reasoning with Scientifically Validated Relationships

• URL: http://arxiv.org/abs/2510.07231v2
• Date: Thu, 09 Oct 2025 16:46:30 GMT
• Title: Benchmarking LLM Causal Reasoning with Scientifically Validated Relationships
• Abstract summary: Causal reasoning is fundamental for Large Language Models to understand genuine cause-and-effect relationships.<n>Existing benchmarks suffer from critical limitations such as reliance on synthetic data and narrow domain coverage.<n>We introduce a novel benchmark constructed from casually identified relationships extracted from top-tier economics and finance journals.
• Score: 21.03106821967686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal reasoning is fundamental for Large Language Models (LLMs) to understand genuine cause-and-effect relationships beyond pattern matching. Existing benchmarks suffer from critical limitations such as reliance on synthetic data and narrow domain coverage. We introduce a novel benchmark constructed from casually identified relationships extracted from top-tier economics and finance journals, drawing on rigorous methodologies including instrumental variables, difference-in-differences, and regression discontinuity designs. Our benchmark comprises 40,379 evaluation items covering five task types across domains such as health, environment, technology, law, and culture. Experimental results on eight state-of-the-art LLMs reveal substantial limitations, with the best model achieving only 57.6\% accuracy. Moreover, model scale does not consistently translate to superior performance, and even advanced reasoning models struggle with fundamental causal relationship identification. These findings underscore a critical gap between current LLM capabilities and demands of reliable causal reasoning in high-stakes applications.

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