It feels like those are on different ends of a spectrum. On one side the Judea Pearl style causality analysis relies on human interpretation because we must adjoint the graphical network with an &quot;assumption graph&quot; which allows for the identification of suitable ad hoc experiments. This pretty much requires a physical semantics be applied to the PGM.<p>On the other hand, neural nets tend to eschew human interpretation. They optimize their objectives, but the resulting networks are rarely amenable to human interpretation. To try to come in after the fact and seek out experimental opportunities seems certain to fail.<p>On the other hand, it&#x27;d be interesting to see if the human assumption graph could be used as a training constraint for the ANN. I have never heard of anyone trying that.