[p4wnc6]

I agree with your distinction between groups who care about "causal inference" like the debates between Judea Pearl and Andrew Gelman on the role of toy problems in statistics, and groups who care more about "prediction engineering" (as long as we are careful to also admit that people in the ML prediction engineering camp care very, very much about the theoretical properties of their methods, especially in avoiding overfitting, because engineering predicition in a climate of overfitting is useless).

I would just add a big third category that probably encompasses the vast majority of people who "work in statistics" and that would be people who are not interested in causal inference nor in predictive efficacy but are interested in a much less rigorous idea of "explanatory modeling" -- and this group generally is very poor with statistical hygiene.