Arguably one reason causality research in the machine learning community hasn't boomed is there is no framework/ease of access to quickly code up the current heuristics/patterns/graph models like you can do with a deep learning idea using pytorch. Deep learning has reached today's stage because of early frameworks like Theano and Caffe. Access to beginners in a field is crucial for SOTA development which although feels a little counter-intuitive is nevertheless true. If you search for causality you get a bunch of books and papers from Pearl and Scholkopf which are fun for reading but what do I do something actionable with that quickly.
Arguably one reason causality research in the machine learning community hasn't boomed is there is no framework/ease of access to quickly code up the current heuristics/patterns/graph models like you can do with a deep learning idea using pytorch.
Ironically enough, it seems like you're confusing cause and effect here. The reason that there's little causation based reasoning isn't because there's no automation for it. Rather, the reason there's no automation for it is because it hasn't boomed.
The reason deep learning based ML for image recognition boomed is because you could take a fairly database of images and categorizations and produce an impressive and testable system using straight forward if challenging optimization procedures. Because this approach has boomed, huge amounts of money have flowed to it, lot of people have been hire, and it's been semi-automated and so you have a combination of data and frameworks that let you things quickly. Some high percentage of all the achievement of current ML is leveraging the original static ability to sort images (or sort buckets of bits) into different areas (Alpha go - sorts moves into "good" and "bad" and adds tree pruning, etc). Which isn't to discount it, it's the first sort of system can seem "as good as human" in certain areas.
But when there's no similarly easy and impressive procedure for taking, say, a time series, and getting the next result better than human or traditional statistics can predict, there's no boom, no gathering of public data sets, no easy automation of the standard procedures and so-forth.
The 2012 Imagenet results which jumpstarted DL did not benefit from Theano or Torch frameworks. Alex Krizhevsky had developed his own GPU accelerated framework (cuda-convnet), and it remained quite popular for a couple of years after the competition, until Theano and Torch caught up with it.
