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Computational Ecology and Software, 2021, 11(4): 154-161
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Article

Causality inference of linearly correlated variables: The statistical simulation and regression method

WenJun Zhang
School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China

Received 11 October 2021;Accepted 17 October 2021;Published 1 December 2021
IAEES

Abstract
Causality inference of variables is a research focus in science. Due to its importance, a statistical simulation and regression method for causality inference of linearly correlated (scale or interval) variables was proposed in present study. First, a statistical simulation and regression method was developed to generate and analyze artificial data of linear correlated variables with known causality. The rule was drawn from the simulation and regression analysis on artificial data. Finally, causality inference of two linearly correlated variables was conducted based on the rule. Full Matlab codes of the method were presented.

Keywords causality;inference;linear dependency;scale or interval variables;Pearson correlation;statistical simulation;regression analysis.

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