测试大模型在因果模型中量化参数估计能力，发现结果波动大且易受结构影响。
Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
- 将因果图分解为父子节点对，逐个提示模型生成回归方程
- 在7个真实因果图上测试，模型系数估计差异大且敏感于结构变化
- 开源工具链，适合关注安全关键领域因果推理的研究者
大语言模型在识别定性因果关系方面已展现潜力，但在连续域中进行定量因果推理——即估计参数化函数关系的效应大小——仍研究不足。我们提出Linear-LLM-SCM，一个即插即用的框架，用于评估在给定有向无环图（DAG）时，大模型对线性高斯结构性因果模型的参数化能力。该框架将DAG分解为局部父-子集合，对每个节点提示模型生成类似回归的结构方程，并聚合结果与真实参数对比。我们在7个真实世界因果图上进行了实验，结果揭示了大模型作为定量因果参数化工具的局限性：系数估计存在显著变异，且对结构扰动高度敏感。我们已开源该框架，以推动社区在医疗等安全关键领域开展大模型用于因果效应提取的研究。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reasoning---estimating effect sizes that parametrize functional relationships---remains underexplored in continuous domains. We introduce Linear-LLM-SCM, a plug-and-play framework for evaluating LLMs on Linear Gaussian structural causal model parametrization when a directed acyclic graph (DAG) is given. The framework decomposes a DAG into local parent-child sets and prompts an LLM to produce a regression-style structural equation per node, which is aggregated and compared against available ground-truth parameters. Our experiments with seven real-world DAGs effect ground truth illustrate limitations of LLMs as quantitative causal parameterizers. Across most models, we observe variability in coefficient estimates and sensitivity to structural perturbations. We open-sourced the framework to further encourage the community to work on studies toward the use of LLM for causal effect elicitation in safety-critical domain, e.g., healthcare.
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