MI is quite useful and widely used. It typically requires binning data though when distributions are unknown / empirically estimated. This approach is a rank-ba

MI is quite useful and widely used. It typically requires binning data though when distributions are unknown / empirically estimated. This approach is a rank-based score, more similar to Spearman correlation than Pearson. This allows for nonlinear relationships between the two variables. A slightly critical review on the work van be seen here: https://academic.oup.com/biomet/advance-article/doi/10.1093/biomet/asab028/6259083 https://academic.oup.com/biomet/advance-article/doi/10.1093/.... They argue that the older forms of rank correlation, namely D, R, and tau*, are superior. Nonetheless, it seems like a nice contribution to the stats literature, although I doubt the widespread use of correlation is going anywhere.