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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 causeandeffect 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.

Ключевые фразы: finite classification task, cause-and-effect relationships, MACHINE LEARNING
Автор (ы): Грушо Александр Анатольевич, Грушо Николай Александрович, Забежало Михаил, Самойлов Константин Э., Тимонина Елена Евгеньевна
Журнал: DISCRETE AND CONTINUOUS MODELS AND APPLIED COMPUTATIONAL SCIENCE

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Идентификаторы и классификаторы

УДК
004.8. Искусственный интеллект
Для цитирования:
ГРУШО А. А., ГРУШО Н. А., ЗАБЕЖАЛО М., САМОЙЛОВ К. Э., ТИМОНИНА Е. Е. STATISTICAL CAUSALITY ANALYSIS // DISCRETE AND CONTINUOUS MODELS AND APPLIED COMPUTATIONAL SCIENCE. 2024. № 2, ТОМ 32
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