  Granger-Geweke causality (GGC) is a powerful and popular method for
identifying directed functional (`causal') connectivity in neuroscience. In a
recent paper, Stokes and Purdon [1] raise several concerns about its use. They
make two primary claims: (1) that GGC estimates may be severely biased or of
high variance, and (2) that GGC fails to reveal the full structural/causal
mechanisms of a system. However, these claims rest, respectively, on an
incomplete evaluation of the literature, and a misconception about what GGC can
be said to measure. Here we explain how existing approaches (as implemented,
for example, in our popular MVGC software [2,3]) resolve the first issue, and
discuss the frequently-misunderstood distinction between functional and
effective neural connectivity which underlies Stokes and Purdon's second claim.
  [1] Patrick A. Stokes and Patrick. L. Purdon (2017), A study of problems
encountered in Granger causality analysis from a neuroscience perspective,
Proc. Natl. Acad. Sci. USA 114(34):7063-7072.
  [2] Lionel Barnett and Anil K. Seth (2012), The MVGC Multivariate Granger
Causality Matlab toolbox, http://users.sussex.ac.uk/~lionelb/MVGC/
  [3] Lionel Barnett and Anil K. Seth (2014), The MVGC multivariate Granger
causality toolbox: A new approach to Granger-causal inference, J. Neurosci.
Methods 223:50-68
