Granger Causality

Granger causality is a statistical method used to infer directional relationships between time series, aiming to determine if one variable's past values help predict another's future. Current research focuses on extending Granger causality to handle nonlinear systems and high-dimensional data, employing diverse approaches such as neural networks (including recurrent and transformer architectures), Bayesian methods, and variations of Hawkes processes. These advancements are improving causal inference in complex domains like climate science, neuroscience, finance, and industrial process optimization, leading to more accurate predictions and a deeper understanding of system dynamics.