Applying ensemble modelling to VAR models It is ok to apply ensemble modeling to VAR models? I mean, using several specifications of the VAR model instead of just one specification. So, for example, if you want to check that $x_{t-1},...,x_{t-p}$ granger causes $y_t$, you compare results across the different specifications. If you have 5 specifications and 4 of them say yes, and 1 says no, then we conclude that $x_{t-1},...,x_{t-p}$ granger cause $y_t$. If 4 of the specifications say no, and 1 says yes, we conclude that $x_{t-1},...,x_{t-p}$ do not granger cause $y_t$. This idea is from machine learning. Not sure if there is a paper out there that has used something similar.
Emsemble learning is beneficial in forecasting where statistical adequacy of any model in the ensemble is of limited importance. However, when models are used for inference, statistical adequacy is quite important. The correctnes of the assumed null distribution of the test statistic depends (to a larger or smaller degree) on the statistical adequacy of the model. Violations of some modelling assumptions may lead to distortions of the critical values and thus the test size, making inference unrealiable. This is a common problem known from the model averaging literature. Only statistically adequate models may be averaged when the goal is inference (rather than forecasting), as otherwise the inference is unreliable. My explanation is a bit simplistic, but I hope it conveys the main idea.