[mlthoughts2018]

This comment doesn't really make much sense for me, especially since none of Pearl's techniques have been convincingly demonstrated to work in real situations. It's one thing to take pot shots at practical engineering problems and point of flaws and locations for improvement, but it's quite different to claim that a new framework would solve them when (a) elements of that framework have already existed a while and practitioners knew about them, and (b) the framework hasn't been shown to give state of the art performance or to actually solve cases when algorithmic decision making made improper judgments.

Do you have examples to dispute this... actual examples where a causal inference based model was used for large-scale deployed machine learning problems and demonstrably fixed some type of judgment error that had previously been leading to bad outcomes for people?