Not sure if people do this, but with a mechanism of action in place you can state a prior belief and turn your AB testing results into actual posteriors instead of frequentist metrics like p-values which are kind of useless.
Also, frequentism / Bayesianism is orthogonal to causal / correlational interpretations.
Having a control doesn't mean you can't fall victim to this.
Observational ones are particularly prone because you can slice and dice the world into near-infinite observation combinations, but people often do that with AB tests too. Shotgun approach, test a bunch of approaches until something works, but if you'd run each of those tests for different significance levels, or for twice as long, or half as long, you could very well see the "working" one fail and a "failing" one work.