You're right Bayesian Networks don't hold any information about real causality, it just assumes one random variable directly influences another random variable , and the joint distribution of those variables tells us the second variable also directly influences the first one. They are just two **Mathematical** points of view, which gives the same result.

However, in some situations (and we can force those situations to happen) we have something more than just the Joint Distribution, we have the [Do-Calculus][1] formulated by Juda Pearl that gives us information about how the variables (or the network) behavior under external intervention. The main concept can be captured when you try to answer: 

*P(X | do(Y=y))* = ?

Where *do(Y=y)* is the action of externally forcing *Y* to be *y* ignoring that *Y* depends only on its parents. That gives us more information about the REAL CAUSAL structure of the Bayesian Network. When you have what is called sometimes by Intervening Data, you can infere the real causal structure more accurately.


  [1]: http://ftp.cs.ucla.edu/pub/stat_ser/r402.pdf