Is Granger causality still relevant? Staying abreast with statistics publications is no small feat, but I did put effort into scoping out what causality papers were coming out. The most recent Granger causality paper I came across was from 2013. During the time I did this research I came across a lot of scorn regarding Granger causality; here is just one of the examples:

My mom Granger causes my birthday every year by baking a birthday cake
  the night before the celebration

There was clearly a degree of mockery in many of such comments. It's hard it take the pulse of all econometricians on this issue, but it seems that with at least part of the community, Granger causality is falling out of favor. 
Question
Is Granger Causality still relevant? If not, is there a legitimate reason, or are the critics just being snobbish/pompous/? By relevant I mean is there actually a niche for it: the old "better than nothing causality" or has it been rendered obsolete by something else?
Further Clarifications:


*

*Scope: econometrics (Granger himself asserted that Granger Causality applied in other fields is not advised)

 A: If you mean econometric theory, I would not be surprised to learn that most of the relevant and needed theoretical results related to the notion of Granger causality have already been derived. After all, the notion of Granger causality is nearly 50 years old. Hence, I do not expect to see much research on the theoretical aspects of Granger causality any more.
If you mean applied econometrics, it seems to me Granger causality is still relevant. 


*

*For example, a popular topic in finance is modelling volatility and market interconnectedness. Granger causality is widely used for examining whether volatility spills over from one market to another (not giving concrete references here, but there are plenty of hits from joint search of "volatility spillover" and "Granger causality", see here; I personally have read a few random papers employing Granger causality for analyzing volatility spillovers).
If the volatility in market $j$ can be predicted using the past information on market $j$ alone, there is little reason to think it is being transmitted from some other market $i$ to market $j$. However, if the volatility in market $j$ is predictable by past information on market $i$, once past information on market $j$ has been accounted for, which means there is Granger causality, it might be though that volatility is transmitted from market $i$ to market $j$.
If you add an assumption that this relationship is causal, you have a causal explanation of volatility transmission. As Judea Pearl notes in "Statistics and causal inference: A review" (2003), 

Every claim invoking causal concepts must be traced to some premises that invoke such concepts; it cannot be derived or inferred from statistical associations alone

Hence, adding a causal premise to the statistical machinery of Granger causality seems fine to me.

*Another example of a popular use of Granger causality is the analysis of lead-lag relationships between markets, see some search results here.
Regarding the birthday cake example, it is false, as are other similar ones I have encountered. The cake has absolutely no predictive power beyond the history of your birthdays: past birthdays contain all possibly relevant information for predicting future birthdays.

Side note: Google Scholar finds 1290 articles from 2018 alone citing Granger's original paper from 1969. This is however not confined to econometrics journals alone.
A: "Granger causality" has nothing to do with causality. It is just that the late Sir Clive Granger was a master of marketing his tremendously important work by tremendously catchy names.  
We should remember that prof. Granger was heavy into forecasting. And in forecasting, if I can forecast your birthday through your (or my) mom, I am perfectly fine. Meaning, that in forecasting we really don't give a dime or a damn about causality under the proper meaning of the word.
When $X$ "Granger causes" $Y$, it just means that forecasts of $Y$ are better if we incorporate $X$ in the forecast function, than if we don't, usually in some reduced-variance sense. That's all.
Now this is, or should be, very well known in scholarly circles. So anyone mocking the concept, while strictly speaking is totally justified because it indeed abuses the word "causality", prove themselves to be below-par by selecting a non-target to mock. It is like seeing an actor pretending to be the Pope, and mocking him because he is not really the Pope (while the important issue is how convincingly he imitates the Pope).
