Semantic causality evaluation of correlation analysis utilizing large language models

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dc.contributor.authorDudáš, Adam
dc.date.accessioned2026-05-04T07:45:30Z
dc.date.available2026-05-04T07:45:30Z
dc.date.issued2026
dc.descriptionIn: Computers, Materials & Continua. Henderson : Tech Science Press, 2026. ISSN 1546-2218. Vol. 87, no. 2 (2026), pp. 1-24.
dc.description.abstractIt 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.
dc.description.sponsorshipUGA UMB UGA-14-PDS-2025 Korelačné štruktúry a vizuálna analýza vzorov v mnohorozmerných datasetoch
dc.identifier.doihttps://doi.org/10.32604/cmc.2026.076507
dc.identifier.issn1546-2218
dc.identifier.issn1546-2226
dc.identifier.urihttps://repo.umb.sk/handle/123456789/1465
dc.language.isoen
dc.publisherTech Science Press : Henderson
dc.rightsCC BY Creative Commons Attribution 4.0. International
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectkorelácie
dc.subjectcorrelations
dc.subjectkauzalita
dc.subjectcausality
dc.subjectkorelačné analýzy
dc.subjectcorrelation analysis
dc.subjectveľké jazykové modely
dc.subjectlarge language models
dc.subjectvizualizácia
dc.subjectvisualization
dc.titleSemantic causality evaluation of correlation analysis utilizing large language models
dc.typeArticle
dc.typeinfo:eu-repo/semantics/article

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