  Since its introduction to the public, ChatGPT has had an unprecedented
impact. While some experts praised AI advancements and highlighted their
potential risks, others have been critical about the accuracy and usefulness of
Large Language Models (LLMs). In this paper, we are interested in the ability
of LLMs to identify causal relationships. We focus on the well-established
GPT-4 (Turbo) and evaluate its performance under the most restrictive
conditions, by isolating its ability to infer causal relationships based solely
on the variable labels without being given any context, demonstrating the
minimum level of effectiveness one can expect when it is provided with
label-only information. We show that questionnaire participants judge the GPT-4
graphs as the most accurate in the evaluated categories, closely followed by
knowledge graphs constructed by domain experts, with causal Machine Learning
(ML) far behind. We use these results to highlight the important limitation of
causal ML, which often produces causal graphs that violate common sense,
affecting trust in them. However, we show that pairing GPT-4 with causal ML
overcomes this limitation, resulting in graphical structures learnt from real
data that align more closely with those identified by domain experts, compared
to structures learnt by causal ML alone. Overall, our findings suggest that
despite GPT-4 not being explicitly designed to reason causally, it can still be
a valuable tool for causal representation, as it improves the causal discovery
process of causal ML algorithms that are designed to do just that.
