In social sciences (speaking as an economist), the goal of statistics was always testing theories and hypotheses against empirical evidence. Once you have a so

In social sciences (speaking as an economist), the goal of statistics was always testing theories and hypotheses against empirical evidence. Once you have a solid theory, then you have an understanding of the subject matter, which allows you to make predictions and counterfactual hypotheses. So the primary goal was understanding the data generating process, finding causal relationships between variables, and so on. The goal was specifically not making predictions. One of the first things I learnt was that a bad model can make good predictions (for a while). The ML crowd appears to be taking the opposite approach, focusing on making predictions and disregarding everything else. I suppose it has its uses, but I wouldn't call it science.