Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques.

基于真实数据，我们提出了一个灵活的评估框架，在评估因果结构和因果效应的差异方面具有关键属性，可以提高大型语言模型的可解释性。我们引入了基于真实数据的开放式因果发现基准（OCDB），以促进公平比较和算法优化。实验结果显示，现有算法在真实数据上存在显著的泛化能力不足，突出了性能改进的潜力以及我们框架在推进因果发现技术方面的重要性。

OCDB：重新审视因果推断的全面基准和评估框架