You should be asking why "statistics" and "machine learning" are different fields. It is a good question!

edit: To clarify, stats is a "service field" for a lot of empirical fields, so lots of them use stats methods and not ML methods. More comp. sci. aligned areas also use more ML e.g. computational bio. There's been a lot of cross fertilization lately, and stats and ML are converging, but the fact that there is "department level division" is supremely weird.

Neural networks are non-linear regression.

Arguably we often can't usefully interpret statistical models unless they correspond to causal ones! One of the historical differences between ML and stats is that the latter was always concerned about experiments and interpretability, and thus about causal matters, whereas the former more about prediction and fancy algorithms.

It is very weird to me ML is so little interested in causality.