Wiener-Granger causality (WGC) is a well-established method for causal inference based on simultaneously recorded time-series. Being (largely) data-driven, it is well suited for investigations of the relations and interactions between network structure and network dynamics. In this talk, I will rehearse the basic properties and implementations of WGC, using an example a recent application to human electroencephalographic (EEG) data obtained in wakefulness and during anesthetic loss-of-consciousness. I will then describe some recent theory and modeling developments, including Gaussian equivalence with transfer entropy, extension to multivariate (or ‘block’) causality, interaction with common data preprocessing methods such as downsampling and filtering (with a special focus on functional MRI), WGC-based measures of ‘autonomy’ and ‘emergence’, and finally network-based measures of ‘causal density’ and ‘integrated information’. I will finish by introducing a radically-revised MATLAB toolbox for implementing WGC on general time-series data.