Why? If you're talking about epidemiological studies looking for associations between variables in big datasets, okay. But that's just one type of study, and it doesn't get better by throwing many of them together in a meta study.
You absolutely can show causality in a single study by doing experiments.
I'm feeling rather the opposite really, "correlation is not causation" is a beaten horse and will come up in every discussion where it may or may not fit on this website.
In many cases, correlation is a hint at causality, and if there's a strong model explaining it, should be considered evidence for causality.
This is the key that everybody ignores.
If you simply throw variables together, you wind up with too many confounders. You can also get the direction wrong.
"Growing tall causes you to be male" is my go to example. The correlation is super powerful--because it's completely backwards. It is also competely valid unless you have a model to differentiate--at which point it becomes obviously non-sensical.
Not saying studies results are invaluable. They can certainly point to the right direction if done right. But just a single RCT won't be enough to prove causality.