Detecting Causality in Complex Dynamic Systems

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For many years the behavior of complex dynamical systems has been a challenge to science. One of the most indisputable obstacles is detecting the causal interactions between components of complex systems, especially in real-world applications. By using cutting-edge technology, we investigated two new causal discovery techniques, the CCM, and the PCMCI method. Both methods were applied to three datasets from real-world phenomena. The first dataset is the EEG dataset and we found strong causal interactions between brain regions in average control brain compared to average alcoholic brain. The second dataset is the Portal ecosystem and our methods found a positive causal influence between ants and the invader plant. Finally, we applied the methods on the Covid-19 cases dataset in Saudi Arabia, and we discovered that there is no evidence of a link between travelling and the spread of Coronavirus cases. Additionally, we compared the outputs of the methods from the first two datasets to the results of a recent study, and we found many similarities between these methods. Moreover, we addressed the strength and weaknesses of each technique in our discussion.