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Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
C B T Versus Treatment as Usual, May Affect the Outcome
The first step is to understand how the data were generated. So for example, i know that it was that c b t was given with a flip of a coin with point five probability of getting c b t. And so ok, so, yes, that's step two. And then step three is to translate the research question into a causal parimeter. The conditional average treatment effect is a causal perameter because it's a function of it has counteract outcomes in it. It can be seen as like a triangle, but without an edge on one side, so that the covers affect the outcome, but doesn't affect the treatment dor the intervention. Ok? That is my causal model
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