A few headings down:
> Causal inference provides us with tools that allow us to answer the question of why something happens.
This is not necessarily so.
Randomized controlled trials suffer from black box problems the same as models. This is clear enough when thinking about something like a tutoring program. Suppose I randomly assign a bunch of schools to learn algebra with curriculum X and the rest to continue business as usual.
Program X does better, so we infer the program has a causal impact on algebra learning.
However, we still do not know for sure why program X does better, only that it does better. This is important to inform how to take what works about the program and apply it to other circumstances, adapt it, and so on.
I suppose compared to a big data set, we have a better "why" answer to the variation between the outcome and the treatment. The difference being that we actually know the cause of the observed effect with a trial, whereas with correlational analyses we're not so sure. But that's a very deflationary view of "why." I don't mean to be too cynical here; we can always push "real" causality one more level down. For example, suppose we figure out the secret sauce to better algebra teaching relates to a specifical pedagogical practice. We can then say "but why does that practice work? what does it do in the brain?" So I don't want be too reductive.
But even gold standard RCTs don't always give us a "why?" answer. I remember attending a conference about a decade ago among causal inference-devoted social researchers specifically about "the black box" of causal inference as it pertains to RCTs.