[nickhuh]

I agree, but I'd expand on that and say that BPL and Probabilistic programming in general benefits hugely from the fact that it is interpretable and thus useful in ways that deep learning is not quite as useful. On top of that, the integration of causal concepts allows the program to generalize to completely novel situations in ways that it's unclear whether it nor other techniques can. For example, can we teach a neural net that if the sun didn't rise tomorrow people would still go to work at 9am? (As long as they weren't too freaked out of course :-) )