Causality and Generalized Impulse Response Functions

Neil Shephard just gave a fine talk, "Econometric analysis of potential outcomes time series:
instruments, shocks, linearity and the causal response function".  Recording here (soon). 
Slides here.  The key result is on slide 11:  If the conditions (Assns 1-3 on slides p. 5) for a potential outcome time series are satisfied, then the Koop-Pesaran-Potter (1996) "generalized impulse response function" (GIRF) has a direct causal interpretation. Neil pitched the paper as providing deeper understanding and firmer foundations for the GIRF, which it certainly does.

Wow! This is wonderful in general, and for me personally:  Throughout almost all my work with Kamil Yilmaz on measuring network connectedness (e.g., here), we work in a GIRF framework for the underlying vector autoregression (actually generalized variance decomposition, but it's the same thing). We liked the GIRF for a pragmatic reason -- its invariance to variable ordering, unlike Cholesky factor identification -- but we always wanted to understand it more deeply.