Granger causality to measure causality strength It is possible to use the Granger causality (GA) test to measure the strength of the causal relation between two time series? More specifically, if we have 3 time series A, B, and C and we have:

*

*p-value GA(A, B) = 0.04

*p-value GA(A, C) = 0.02

Can we conclude that the causal relation between A and C is twice stronger the causal relation between A and B? If not, why are the p-values not good to measure the strength of the relation and how could we measure the strength of the relation instead?

I am not sure there is a standard terminology regarding the strength of Granger causality. We could refer to "effect size" from regression modelling and define strength of Granger causality as the relevant effect size. Then to learn about the strength of Granger causality from $X$ to $Y$ you would look at the point estimates of the coefficients on lags of $X$ in the equation of $Y$. Large (in absolute value) point estimates signify a pronounced Granger-causal relationship.
As Henry said in the comments, $p$-values do not directly measure the strength of a relationship. They account for measurement precision in addition to strength. A relationship with a small point estimate can still have a small $p$-value if the estimation precision is high.
