Deciphering interventional dynamical causality from non-intervention systems

2024-06-29

arXiv.org

AI Summary

To address the challenge of reliably identifying causal relationships from purely observational time series in non-interventional complex systems, this paper proposes the Interventional Dynamical Causality (IntDC) framework and the Interventional Embedding Entropy (IEE) criterion. IEE integrates time-delay embedding, causal graph learning, and robust statistical estimation—grounded in differential geometric embedding and information-theoretic entropy—enabling the first rigorous, computable definition of interventional causal strength directly from observational data, without requiring knowledge of underlying dynamical models or physical interventions. Unlike Granger causality and transfer entropy, IEE relaxes restrictive assumptions of linearity, stationarity, and experimental intervention. Experiments on the *C. elegans* neural connectome, Japan’s COVID-19 transmission network, and circadian gene regulatory networks demonstrate causal identification accuracy exceeding 92%, significantly outperforming existing non-interventional causal inference methods.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Detect causality in non-intervention complex systems
• Measure causal strength without dynamical models
• Rank causal effects and construct networks

Innovation

• Delay-embedding technique for causality detection
• Interventional Embedding Entropy (IEE) measures causal strength
• Decipher causality from non-interventional time-series data