Causal networks are widely used in many fields, including epidemiology,
social science, medicine, and engineering, to model the complex relationships
between variables. While it can be convenient to algorithmically infer these
models directly from observational data, the resulting networks are often
plagued with erroneous edges. Auditing and correcting these networks may
require domain expertise frequently unavailable to the analyst. We propose the
use of large language models such as ChatGPT as an auditor for causal networks.
Our method presents ChatGPT with a causal network, one edge at a time, to
produce insights about edge directionality, possible confounders, and mediating
variables. We ask ChatGPT to reflect on various aspects of each causal link and
we then produce visualizations that summarize these viewpoints for the human
analyst to direct the edge, gather more data, or test further hypotheses. We
envision a system where large language models, automated causal inference, and
the human analyst and domain expert work hand in hand as a team to derive
holistic and comprehensive causal models for any given case scenario. This
paper presents first results obtained with an emerging prototype.

利用 ChatGPT 这样的大型语言模型作为因果网络的审计员，与人类分析师和领域专家共同合作，利用自动因果推理方法对因果网络进行审查和纠正，以获取与边缘方向、可能的混淆因素和中介变量相关的见解，进而为任意情景构建全面综合的因果模型。