Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

Complex systems are challenging to understand, especially when they defy manipulative
experiments for practical or ethical reasons. Several fields have developed parallel …

While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …

This study employs a fresh perspective to investigate the causal relationship between
uncertainty, measured via the two proxies of Economic Policy Uncertainty (EPU) and the …

Mental disorders are among the leading causes of disability worldwide. The first step in
treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms …

Many natural and man-made complex dynamical systems can be represented by networks
with vertices representing system units and edges the coupling between vertices. If edges of …

We propose a novel multi-scale neural network for Granger causality discovery (MSNGC) in
multivariate time series. Compared with existing counterparts, our model avoids the explicit …

Understanding the underlying processes and extracting detailed characteristics of
spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of …

Convergent cross mapping is a principled causal discovery technique for signals, but its
efficacy depends on a number of assumptions about the systems that generated the signals …

Research on concepts and computational methods of causality has a long history, and there
are various concepts of causality as well as corresponding computing algorithms based on …