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Causal networks are widely used in many fields, including epidemiology,social science, medicine, and engineering, to model the complex relationshipsbetween variables. While it can be convenient to algorithmically infer thesemodels directly from observational data, the resulting networks are oftenplagued with erroneous edges. Auditing and correcting these networks mayrequire domain expertise frequently unavailable to the analyst. We propose theuse 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, toproduce insights about edge directionality, possible confounders, and mediatingvariables. We ask ChatGPT to reflect on various aspects of each causal link andwe then produce visualizations that summarize these viewpoints for the humananalyst to direct the edge, gather more data, or test further hypotheses. Weenvision a system where large language models, automated causal inference, andthe human analyst and domain expert work hand in hand as a team to deriveholistic and comprehensive causal models for any given case scenario. Thispaper presents first results obtained with an emerging prototype.

An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development