Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)4138910.22363/2658-4670-2024-32-2-213-221CPUADEArticlesСтатьиResearch ArticleStatistical causality analysisСтатистический анализ причинно-следственных связейhttps://orcid.org/0000-0003-4400-2158GrushoAlexander A.ГрушоА. А.

Principal scientist, Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences; professor of Department of Probability Theory and Cyber Security of Peoples’ Friendship University of Russia named after Patrice Lumumba

grusho@yandex.ruhttps://orcid.org/0000-0002-5005-2744GrushoNikolai A.ГрушоН. А.

Candidate of Physical and Mathematical Sciences, Senior scientist, Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

info@itake.ruhttps://orcid.org/0000-0002-5067-5909ZabezhailoMichael I.ЗабежайлоМ. И.

Professor, Doctor of Physical and Mathematical Sciences, Principal scientist, Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

m.zabezhailo@yandex.ruhttps://orcid.org/0000-0002-6368-9680SamouylovKonstantin E.СамуйловК. Е.

Professor, Doctor of Technical Sciences, Head of the Department of Probability Theory and Cyber Security of Peoples’ Friendship University of Russia named after Patrice Lumumba

samuylovke@rudn.ruhttps://orcid.org/0000-0002-6493-3622TimoninaElena E.ТимонинаЕ. Е.

Professor, Doctor of Technical Sciences, Leading scientist, Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences; professor of Department of Probability Theory and Cyber Security of Peoples’ Friendship University of Russia named after Patrice Lumumba

eltimon@yandex.ruFederal Research Center “Computer Sciences and Control” of the Russian Academy of SciencesФедеральный исследовательский центр «Информатика и управление» РАНRUDN UniversityРоссийский университет дружбы народов15102024322VOL 32, NO2 (2024)ТОМ 32, №2 (2024)21322101112024Copyright ©; 2024, Grusho A.A., Grusho N.A., Zabezhailo M.I., Samouylov K.E., Timonina E.E.Copyright ©; 2024, Грушо А.А., Грушо Н.А., Забежайло М.И., Самуйлов К.Е., Тимонина Е.Е.2024Grusho A.A., Grusho N.A., Zabezhailo M.I., Samouylov K.E., Timonina E.E.Грушо А.А., Грушо Н.А., Забежайло М.И., Самуйлов К.Е., Тимонина Е.Е.https://creativecommons.org/licenses/by-nc/4.0https://journals.rudn.ru/miph/article/view/41389

The problem of identifying deterministic cause-and-effect relationships, initially hidden in accumulated empirical data, is discussed. Statistical methods were used to identify such relationships. A simple mathematical model of cause-and-effect relationships is proposed, in the framework of which several models of causal dependencies in data are described - for the simplest relationship between cause and effect, for many effects of one cause, as well as for chains of cause-and-effect relationships (so-called transitive causes). Estimates are formulated that allow using the de Moivre-Laplace theorem to determine the parameters of causal dependencies linking events in a polynomial scheme trials. The statements about the unambiguous identification of causeand-effect dependencies that are reconstructed from accumulated data are proved. The possibilities of using such data analysis schemes in medical diagnostics and cybersecurity tasks are discussed.

Рассмотрена проблема выявления детерминированных причинно-следственных связей, изначально скрытых в накопленных эмпирических данных. Для выявления таких связей использовались статистические методы. Предложена простая математическая модель причинно-следственных отношений, в рамках которой описано несколько моделей причинно-следственных связей в данных - для простейших отношений между причиной и следствием, для многих следствий одной причины, а также для цепей причинно-следственных связей (так называемых транзитивных причин). Сформулированы оценки, позволяющие с помощью теоремы Муавра-Лапласа определить параметры модели, связывающие события в испытаниях полиномиальной схемы. Обоснованы утверждения об однозначной идентификации причинно-следственных связей, которые восстанавливаются по накопленным данным. Обсуждаются возможности использования таких схем анализа данных в задачах медицинской диагностики и кибербезопасности.

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