Some say some books need to be chewed and digested, but that it is worth the effort. Well, I've been chewing this from time to time since his 2011 Turing prize;

Some say some books need to be chewed and digested, but that it is worth the effort. Well, I've been chewing this from time to time since his 2011 Turing prize; and I have yet to be able to build an understanding around it. Understanding Michael StoneBreaker's was much easier. Build great databases. Several times. Get prizes. It feels like this causality formulation begs for modelling, and for a program to be able to execute and test its own do-calculus actions; to provide the world with a compelling 'Hello World'. Maybe emit some counterfactuals too. Reinforcement Learning seems to yield way better results at exploring the world; at producing actions that help model and test (and then influence) the world. Why is not more used? What's the story around it?