Deciphering interventional dynamical causality from non-intervention systems

• URL: http://arxiv.org/abs/2407.01621v1
• Date: Sat, 29 Jun 2024 03:17:53 GMT
• Title: Deciphering interventional dynamical causality from non-intervention systems
• Authors: Jifan Shi, Yang Li, Juan Zhao, Siyang Leng, Kazuyuki Aihara, Luonan Chen, Wei Lin,
• Abstract summary: We propose a framework named Interventional Dynamical Causality (IntDC) for non-intervention systems. The IEE criterion theoretically and numerically enables the deciphering of IntDC solely from observational (non-interventional) time-series data. Demonstrations of performance showed the accuracy and robustness of IEE on benchmark simulated systems as well as real-world systems.
• Score: 8.787451281894251
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. To address this challenge, we propose a framework named Interventional Dynamical Causality (IntDC) for such non-intervention systems, along with its computational criterion, Interventional Embedding Entropy (IEE), to quantify causality. The IEE criterion theoretically and numerically enables the deciphering of IntDC solely from observational (non-interventional) time-series data, without requiring any knowledge of dynamical models or real interventions in the considered system. Demonstrations of performance showed the accuracy and robustness of IEE on benchmark simulated systems as well as real-world systems, including the neural connectomes of C. elegans, COVID-19 transmission networks in Japan, and regulatory networks surrounding key circadian genes.

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