Semantic Causality Evaluation of Correlation Analysis Utilizing Large Language Models

It is known that correlation does not imply causality. Some relationships identified in the analysis of data are coincidental or unknown, and some are produced by real-world causality of the situation, which is problematic, since there is a need to differentiate between these two scenarios. Until recently, the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data. This has changed with the advance of large language models, which are often utilized as surrogates for such human experts, making the process automated and readily available to all data analysts. This motivates the main objective of this work, which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis, together with its visual analysis model called Causal heatmap. After the implementation itself, the model is evaluated from the point of view of the quality of the visual model, from the point of view of the quality of causal evaluation based on large language models, and from the point of view of comparative analysis, while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets. The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method, supported by the evident highlighting of interesting relationships, while suppressing irrelevant ones.