标题： 基于约束的因果发现的大规模语言模型 显示英文标题

标题： Large Language Models for Constrained-Based Causal Discovery

摘要： 因果关系对于理解复杂系统至关重要，例如经济、大脑和气候。 构建因果图通常依赖于数据驱动或专家驱动的方法，这两种方法都面临诸多挑战。 前者方法，如著名的PC算法，存在数据需求和因果充分性假设的问题，而后者则需要大量时间和领域知识。 这项工作探讨了大型语言模型（LLMs）作为领域专家在因果图生成中的替代能力。 我们将条件独立性查询作为提示提供给LLMs，并使用PC算法处理答案。 在已知因果图的系统中，基于LLM的条件独立性预言机的表现显示出高度的变异性。 我们通过一种提出的受统计学启发的投票方案来提高性能，该方案允许对假阳性率和假阴性率进行一定程度的控制。 检查思维链论证，我们发现因果推理能够为其对概率查询的回答提供依据。 我们展示了基于知识的CIT最终可能成为数据驱动因果发现的补充工具的证据。 显示英文摘要

摘要： Causality is essential for understanding complex systems, such as the economy, the brain, and the climate. Constructing causal graphs often relies on either data-driven or expert-driven approaches, both fraught with challenges. The former methods, like the celebrated PC algorithm, face issues with data requirements and assumptions of causal sufficiency, while the latter demand substantial time and domain knowledge. This work explores the capabilities of Large Language Models (LLMs) as an alternative to domain experts for causal graph generation. We frame conditional independence queries as prompts to LLMs and employ the PC algorithm with the answers. The performance of the LLM-based conditional independence oracle on systems with known causal graphs shows a high degree of variability. We improve the performance through a proposed statistical-inspired voting schema that allows some control over false-positive and false-negative rates. Inspecting the chain-of-thought argumentation, we find causal reasoning to justify its answer to a probabilistic query. We show evidence that knowledge-based CIT could eventually become a complementary tool for data-driven causal discovery.