I agree with the size of the difference yet disagree with the examples as there is ml research considering all 3 (causal ml, conformal ml/predictions/forecasting, AI safety, reliability etc.) I think the difference is more like deduction and induction in a sense, meaning the process of finding the answers are different. Since finishing pooping on corporate time, I will keep this short.

ML: Data -> Method -> Hypothesis -> Answers

Statistics: Hypothesis -> Method -> Data -> Answers

This may be too simplistic and please propose a better distinction but do not postulate that ML does not care about things statistics do.