1. First let's get through the easy part: reinforcement learning (RL) is not unique in its ability to identify cause-and-effect - this was achieved long ago through the use of randomized controlled trials. RL merely streamlines the task of reacting to such information (as well as optimizing experiments w.r.t. a desired goal).

2. Now the trickier part: you can learn causality from observational data alone if you combine this with understanding of a mechanism. Indeed, the whole field of causal inference is an attempt to formalize and extend such methods.

There is a massive space of problems where experimentation (whether old-fashioned A/B testing or more sophisticated online learning) is simply not possible, whether because of ethical reasons, cost, or other reasons behind non-destructive study. These problems are common in medicine, economics, physics. In such problems the only data is observational. Causal inference is very valuable here.