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Exploring and exploiting the seemingly harmless theorem behind Double Machine Studying
21 hours in the past
Causal inference, and particularly causal machine studying, is an indispensable instrument that may assist us make selections by understanding trigger and impact. Optimizing costs, lowering buyer churn, operating focused advert campaigns, and deciding which sufferers would profit most from medical remedy are all instance use circumstances for causal machine studying.
There are various strategies for causal machine studying issues, however the method that appears to face out most is called Double Machine Learning (DML) or Debiased/Orthogonal Machine Studying. Past the empirical success of DML, this system stands out due to its wealthy theoretical backing rooted in a easy theorem from econometrics.
On this article, we’ll unpack the concept that grounds DML by means of hands-on examples. We’ll talk about the instinct for DML and empirically confirm its generality on more and more complicated examples. This text will not be a tutorial on DML, as an alternative it serves as motivation for the way DML fashions see previous mere correlation to know and predict trigger and impact.
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