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You are completely right, I jumped through too many hops to reach that conclusion, and I was very imprecise in stating it. Let me try again: the most interesti
by blackbear_ 6y ago
You are completely right, I jumped through too many hops to reach that conclusion, and I was very imprecise in stating it.
Let me try again: the most interesting and frequent setting is where you cannot control the experimental conditions and you have no idea about the causal mechanism at play. And that is where traditional ML cannot help, but RL can.
- zwaps 6y agoWhat you are describing seems to be essentially the process of science: Develop, step by step, the causal mechanisms at play until you solve the problem. It should not be underestimated that our collective knowledge of causal inference with statistical methods is good, but still improving. I mean, in that sense, ML is helpful. Heck, it is already used in causal inference in two-step estimators and the like.