Statistical causality analysis
Journal: Discrete and Continuous Models and Applied Computational Science (Vol.32, No. 2)Publication Date: 2024-11-02
Authors : Alexander Grusho; Nikolai Grusho; Michael Zabezhailo; Konstantin Samouylov; Elena Timonina;
Page : 213-221
Keywords : finite classification task; cause-and-effect relationships; machine learning;
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.
Other Latest Articles
- Clenshaw algorithm in the interpolation problem by the Chebyshev collocation method
- The recent progress in terahertz channel characterization and system design
- Solution of a two-dimensional time-dependent Schrödinger equation describing two interacting atoms in an optical trap
- Chronology of the development of active queue management algorithms of RED family. Part 3: from 2016 up to 2024
- Marginal asymptotic diffusion analysis of two-class retrial queueing system with probabilistic priority as a model of two-modal communication networks
Last modified: 2024-11-02 04:40:31