CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

Understand

The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals the underlying data distribution.

• However, the lack of a comprehensive benchmark currently limits the evaluation of LLMs' causal learning capabilities.
• To fill this gap, this paper develops CausalBN-Bench based on data from the causal research community, enabling comparative evaluations of LLMs against traditional causal learning algorithms.
• To provide a comprehensive investigation, we offer three tasks of varying difficulties, including correlation, causal skeleton, and causality identification.