C ausal B ench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models (2024)

Abstract: Causal reasoning, a core aspect of human cognition, is essential for advancing large language models (LLMs) towards artificial general intelligence (AGI) and reducing their propensity for generating hallucinations. However, existing datasets for evaluating causal reasoning in LLMs are limited by narrow domain coverage and a focus on cause-to-effect reasoning through textual problems, which does not comprehensively assess whether LLMs truly grasp causal relationships or merely guess correct answers. To address these shortcomings, we introduce a novel benchmark that spans textual, mathematical, and coding problem domains. Each problem is crafted to probe causal understanding from four perspectives: cause-to-effect, effect-to-cause, cause-to-effect with intervention, and effect-to-cause with intervention. This multi-dimensional evaluation method ensures that LLMs must exhibit a genuine understanding of causal structures by correctly answering questions across all four dimensions, mitigating the possibility of correct responses by chance. Furthermore, our benchmark explores the relationship between an LLM’s causal reasoning performance and its tendency to produce hallucinations. We present evaluations of state-of-the-art LLMs using our benchmark, providing valuable insights into their current causal reasoning capabilities across diverse domains. The dataset is publicly available for download at https://huggingface.co/datasets/CCLV/CausalBench

## introduction causal reasoning, the ability to understand and infer causal relationships between variables, is a fundamental aspect of human cognition and plays a crucial role in decision-making, problem-solving, and learning @xcite . for large language models (llms), causal reasoning refers to the ability to accurately identify, represent, and reason about causal relationships described in text, mathematical equations, or code snippets @xcite . developing strong causal reasoning abilities in llms is essential for progress toward artificial general intelligence (agi), as it enables models to understand not just correlations but the underlying mechanisms driving outcomes @xcite . this understanding is crucial for making accurate predictions, generating insightful explanations, and adapting to new situations, as core components of agi. however, existing causal reasoning benchmarks have several limitations that hinder their ability to comprehensively evaluate the causal reasoning capabilities of llms. first, current benchmarks often focus on a single perspective of causal reasoning, such as cause-to-effect, lacking a multifaceted assessment that considers effect-to-cause reasoning and the impact of interventions. this narrow focus allows models to correctly answer causal questions by chance without truly understanding the underlying causal relationships @xcite . second, current benchmarks are primarily text-based, lacking diversity in problem types, such as mathematical and coding problems that can encapsulate causal dependencies. incorporating these diverse problem formats would enable a more robust evaluation of llms' capacity to reason about causality across various modalities. third, the limited scale of existing benchmarks may not provide a sufficiently comprehensive assessment of llms' causal reasoning abilities due to the limited scale of the benchmark dataset. to address these limitations, we propose causal-bench, a comprehensive benchmark for evaluating the causal reasoning capabilities of llms. causal-bench comprises four perspectives of causal reasoning for each scenario: cause-to-effect, effect-tocause, cause-to-effect with intervention, and effectto-cause with intervention. this multi-perspective approach mitigates the potential for correct answers by chance and provides a more accurate evaluation of llms' understanding of causal relationships. moreover, causalbench includes a diverse set of problem types spanning textual, mathematical, and coding domains, enabling a comprehensive assessment of causal reasoning abilities across different modalities. the benchmark consists of more than 60,000 problems and employs six evaluation metrics to measure llms' causal reasoning performance. the major contributions of causalbench are three-fold: (1) evaluating four causal reasoning perspectives per scenario to robustly assess causal understanding, (2) incorporating a diverse problem set spanning math, code, and natural language for cross-modal evaluation, and (3) implementing strict quality control measures, including a causal inference engine check and human expert review, to ensure the benchmark's validity and reliability. by addressing the limitations of existing benchmarks, causalbench aims to provide a more comprehensive and accurate evaluation of the causal reasoning capabilities of llms, facilitating progress towards agi. ## dataset construction process and method the construction of causalbench involves three key steps: manual generation of initial test cases, scaling up using llm such as gpt-4 turbo, and quality control through causal inference engines together with human verification. initially, we manually create a set of test cases covering four aspects of causal inference: (a) cause to effect, (b) effect to cause, (c) cause to effect with intervention, and (d) effect to cause with intervention to ensure a comprehensive evaluation of causal reasoning capabilities from different perspective. to expand the dataset, we then use gpt-4 turbo with fewshot prompting, leveraging the model's ability to generate additional test cases that adhere to the desired format and cover the four causal inference aspects. the few-shot prompts are designed to guide gpt-4 turbo in producing a diverse and extensive set of problems that maintain consistency with the manually generated cases. afterward, we implement a quality control process involving validation through causal inference engines and review by human experts. the causal inference engines verify the logical consistency and correctness of the generated test cases, while human experts review and refine the dataset to maintain high standards of quality and relevance. ## correlation with hallucination to analyze the correlation between llms' causal reasoning ability and their hallucination rate, we referred to the llms' performance on hallucination datasets. the hallucination evaluation results were obtained from the hallucination leaderboard, developed by vectara @xcite . this leaderboard provides a comparison of llm performance in maintaining a low hallucination rate and ensuring factual consistency when summarizing a set of facts. the hallucination evaluation process involves measuring the hallucination rate, factual consistency rate, answer rate, and average summary length. these metrics provide a comprehensive understanding of each model's tendency to hallucinate and its ability to maintain factual accuracy @xcite . after comparing the llms' performance on causalbench with their performance on the hallucination evaluation leaderboard provided by vectara on huggingface (hughes and bae, 2023), we found that models with stronger causal reasoning abilities tend to exhibit lower hallucination rates. for instance, gpt-4 turbo, llama-3-70b, and mistral-7b, which demonstrated superior performance on causal reasoning tasks, also had low hallucination rates. in contrast, models like google gemma-7b-it and llama-2-7b, which showed weaker performance on our causalbench, had higher hallucination rates of 7.5% and 5.6%, respectively. this trend indicates a potential link between a model's ability to understand and reason about causal relationships and its likelihood of not producing hallucinations. further research is required to explore this correlation in more depth and to understand the underlying mechanisms driving this relationship. ## conclusion in this paper, we present causalbench, a comprehensive benchmark dataset for evaluating the causal reasoning capabilities of large language models. causalbench innovatively proposes four types of questioning approaches for each causal scenario: cause-to-effect, effect-to-cause, cause-toeffect with intervention, and effect-to-cause with intervention. by calculating the proportion of cases where models correctly answer all four question types, causalbench effectively assesses whether llms truly understand the underlying causal relationships, mitigating the impact of models coincidentally providing correct answers without causal comprehension. the dataset encompasses a diverse set of problems spanning textual, mathematical, and coding domains, addressing the limitations of existing causal reasoning benchmarks. evaluated on causal-bench, state-of-the-art llms demonstrate stronger performance on mathematical problems compared to textual and coding tasks. notably, models with superior causal reasoning abilities tend to exhibit lower hallucination rates, suggesting a potential link between the two capabilities. despite its contributions, causalbench has several limitations, including the need for expanded domain coverage and deeper exploration of the intrinsic mechanisms connecting causal reasoning and hallucination reduction. future work will focus on addressing these limitations, further refining the evaluation metrics, and providing insights to advance the development of causal reasoning abilities in large language models. causalbench serves as a robust tool and an important step towards achieving artificial general intelligence.