This is only true for simple systems - with more complications you can indeed sometimes deduce causal structure!

Suppose you have three variables: Utopamine conentration, smiling, and reported happiness. And further suppose that there is an independent noise source for each of these variables - causal nodes that we put in as a catch-all for fluctuations and external forcings that are hard to model.

If Utopamine is the root cause of both smiling and reported happiness, then the variation in happiness will be independent of the variation in smiling, conditional on the variation in Utopamine. But conditional on the variation in smiling, the variation in utopamine and reported happiness will still be correlated!

The AI can now narrow down the causal structure to 2, and perhaps it can even figure out the right one if there's some time lag in the response and it assumes that causation goes forward in time.