Statistical causality analysis

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Abstract

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.

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1. Introduction The simplest idea of a causal relationship is given by the functional dependence
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About the authors

Alexander A. Grusho

Federal Research Center “Computer Sciences and Control” of the Russian Academy of Sciences; RUDN University

Author for correspondence.
Email: grusho@yandex.ru
ORCID iD: 0000-0003-4400-2158

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

44 Vavilova St, bldg. 2, Moscow, 119133, Russian Federation; 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Nikolai A. Grusho

Federal Research Center “Computer Sciences and Control” of the Russian Academy of Sciences

Email: info@itake.ru
ORCID iD: 0000-0002-5005-2744

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

44 Vavilova St, bldg. 2, Moscow, 119133, Russian Federation

Michael I. Zabezhailo

Federal Research Center “Computer Sciences and Control” of the Russian Academy of Sciences

Email: m.zabezhailo@yandex.ru
ORCID iD: 0000-0002-5067-5909

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

44 Vavilova St, bldg. 2, Moscow, 119133, Russian Federation

Konstantin E. Samouylov

RUDN University

Email: samuylovke@rudn.ru
ORCID iD: 0000-0002-6368-9680

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

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Elena E. Timonina

Federal Research Center “Computer Sciences and Control” of the Russian Academy of Sciences; RUDN University

Email: eltimon@yandex.ru
ORCID iD: 0000-0002-6493-3622

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

44 Vavilova St, bldg. 2, Moscow, 119133, Russian Federation; 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

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Copyright (c) 2024 Grusho A.A., Grusho N.A., Zabezhailo M.I., Samouylov K.E., Timonina E.E.

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