The main reason for not using causal inference is not because data scientists don’t know about the different approaches or can’t imagine something equivalent (a lot of reinvention); forecasting is one of the most common tasks, after all.

The main reason is that they generally work for software companies where it’s easier and less susceptible to analyst influence to implement the suggested change and test it with a Random Control Trial. I remember running an analysis that found that gender was a significant explaining factor for behavior on our site; my boss asked (dismissively): What can we do with that information? If there is an assumption of how things work that doesn’t translate to a product change, that insight isn’t useful; if there is a product intuition, testing the product change itself is key, and there’s no reason to delay that.

There are cases where RCTs are hard to organize (for example, multi-sided platform businesses) of changes that can’t be tested in isolation (major brand changes). Those tend to benefit from the techniques described there——and they have dedicated teams. But this is a classic case of a complicated tool that doesn’t fit most use cases.