ASSESSING THE DEGREE OF INFLUENCE BETWEEN THE PARAMETERS OF THE STRUCTURAL-CAUSAL MODEL

DOI: 10.31673/2412-4338.2023.030011

Abstract

Cause-and-effect relationships underpin modern science and the ability to teach computers human-like reasoning. The study of causality combined with modern advances in machine learning can greatly accelerate scientific progress. Modern scientific advancements emphasize the importance of not only establishing causality, but also assessing the degree of influence between cause-and-effect relationships.
The work poses an interventionist and counterfactual question: «How will the values of the model parameters change due to the influence of the studied factor?» and «Which factor needs to be influenced for the fastest change in the value of the studied model parameter?». Formulating more precise hypotheses and accelerating their testing can be achieved by answering these questions.
This article proposes a method for estimating the degree of influence between the parameters of the structural-causal model, which solves the problem of identifying the most influential factors, and also improves the study of causal relationships between parameters, which will allow improving the prediction and management of the model under study. The proposed approach of presenting data in a structural-causal model allows you to draw conclusions about what is the cause and what is the effect and take into account the influence of both direct and indirect cause-and-effect relationships. The assessment of the degree of influence determines the most significant parameters and the least influential factors. Consequently, predicting changes in model parameter values becomes more predictable and controllable. Conditional ignoring of the least influential parameters can be used in the model optimization process.

Keywords: causal-structural model, cause-and-effect relationships, machine learning.

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