An Explainable AI Approach to Large Language Model Assisted Causal Model
  Auditing and Development
Yanming Zhang Brette Fitzgibbon Dino Garofolo Akshith Kota Eric Papenhausen Klaus Mueller
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.