Granger Causal Graph

Granger causal graphs aim to infer directional causal relationships between variables in multivariate time series data, offering a powerful tool for understanding complex systems. Current research focuses on improving the accuracy and efficiency of inferring these graphs, employing Bayesian methods, variational autoencoders, and minimum description length principles within various model architectures like vector autoregressions and Hawkes processes. This work has significant implications for diverse fields, enhancing the interpretability of machine learning models, improving the analysis of biological and financial data, and enabling more insightful studies of complex systems like human gait.