Causality inference of linearly correlated variables: The statistical simulation and regression method
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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
International Academy of Ecology and Environmental Sciences
2021-12-01
|
| Series: | Computational Ecology and Software |
| Subjects: | |
| Online Access: | http://www.iaees.org/publications/journals/ces/articles/2021-11(4)/causality-inference-of-linearly-correlated-variables.pdf |
| Summary: | 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. |
|---|---|
| ISSN: | 2220-721X |