Using GPT-4 to guide causal machine learning

TL;DR

This paper evaluates GPT-4's ability to identify causal relationships from label-only data, demonstrating its potential to enhance causal discovery when combined with traditional causal machine learning methods.

Contribution

It shows that GPT-4 can effectively assist in causal graph inference, improving the accuracy of causal ML algorithms and addressing trust issues in causal modeling.

Findings

GPT-4 graphs judged most accurate by participants

Pairing GPT-4 with causal ML improves causal graph quality

GPT-4's causal reasoning enhances traditional causal discovery methods

Abstract

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 other context by humans, 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.