Scientific discovery is a catalyst for human intellectual advances, driven by the cycle of hypothesis generation, experimental design, data evaluation, and iterative assumption refinement. This process, while crucial, is expensive and heavily dependent on the domain knowledge of scientists to generate hypotheses and navigate the scientific cycle. Central to this is causality, the ability to establish the relationship between the cause and the effect. Motivated by the scientific discovery process, in this work, we formulate a novel task where the input is a partial causal graph with missing variables, and the output is a hypothesis about the missing variables to complete the partial graph. We design a benchmark with varying difficulty levels and knowledge assumptions about the causal graph. With the growing interest in using Large Language Models (LLMs) to assist in scientific discovery, we benchmark open-source and closed models on our testbed. We show the strong ability of LLMs to hypothesize the mediation variables between a cause and its effect. In contrast, they underperform in hypothesizing the cause and effect variables themselves. We also observe surprising results where some of the open-source models outperform the closed GPT-4 model.

本研究针对科学发现过程中假设生成的高成本和需要领域知识的挑战，提出了一种新任务，利用部分因果图中的缺失变量生成假设。研究显示，大语言模型在假设因果关系中的中介变量方面表现强劲，但在生成因果变量方面则表现不足，同时一些开源模型在某些方面超越闭源的GPT-4模型。这一发现为科学发现中的因果推理提供了新的视角和工具。

利用大语言模型假设缺失的因果变量