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Granger's causality is a very restrictive and incomplete view of causality. Pearl's counterfactual system with do calculus is a more general way to think about causality. This SURD appears to be a souped up version of Granger.

And the potential outcomes framework (Neyman-Rubin) is even more general :)

Either way, Holland's 'Statistics and Causal Inference' paper (1986) is a nice read on the different frameworks for causality, especially in regards to Granger (&friends) versus do-calculus/Neyman-Rubin.

> Holland's 'Statistics and Causal Inference' paper (1986)

In case anyone else wants to take a look:

https://www.jstor.org/stable/2289064

https://doi.org/10.2307/2289064

Page 2 speaks about the relationship and precedent of GC... Honestly worth a read, this is definitely one of most interesting papers for me in the last few years.

I’ll check it out - so you think this had some noteworthy real world applications?

I think convergent cross mapping came out after Granger causality. Did that ever go anywhere either?