Typically, stats seems to have different usage and teaching by field. Psychologists have their flavor, physicists another. Econometricians, epidemiologists, nut

Typically, stats seems to have different usage and teaching by field. Psychologists have their flavor, physicists another. Econometricians, epidemiologists, nutritionists, etc, all different. They're pretty much all asking causal questions, but they're coming up with different techniques to solve the specific weird questions that are unique to their field. Some fields get to randomize everything. Causal inference is is as simple as asking "is this difference real" when the difference in your experiment would be causal by design. This is what is taught as stats 1 to everybody who starts learning stats. Many fields don't get to randomly do things :(. Maybe you want to know the effect of a disease, but we as a society have decided that harming people to learn for the eventual greater good is bad (hello "rush out the self driving cars!" proponents!). Or maybe randomizing would involve merging a few multi-billion dollar companies and grad students don't have that budget. For whatever reason, you can't intervene in the same way. I give those two examples because epidemiologists and economists have really extended the state of the art of causal inference because their data doesn't look the same as many other fields. Inference is harder (but still possible!). Those niche developments have been happening for a while, but haven't been cohesively un-niched. Big (non-randomized) data is becoming a big thing and everyone wants to use it to make decisions. Decisions require causal statements and most people only know how to make those statements for cleanly randomized stats 1 stuff. People seem to be picking up on the fact that some of this previously niche stuff is much more broadly useful.