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Computational Ecology and Software, 2021, 11(4): 142-153
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Article

Causality inference of nominal variables: A statistical simulation method

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

Received 1 October 2021;Accepted 5 October 2021;Published 1 December 2021
IAEES

Abstract
In present study I proposed a statistical simulation method for causality inference of nominal variables (i.e., categorical variables). A new correlation measure for nominal variables, association coefficient, is firstly proposed also. A statistical simulation method was developed to generate artificial data of nominal variables with known causality. The law was then drawn from the simulation analysis of the artificial data. For a set of data of two nominal variables, the randomization method was first used to test the statistical significance of the nominal correlation measure, and then the statistical simulation was used to determine the causality and its statistic significance of two nominal variables. Full Matlab codes of the method were presented.

Keywords causality;inference;correlation;nominal variables;contingency measures;association coefficient;randomization;statistical simulation;non-parametric statistics.

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