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Exploring and exploiting the seemingly harmless theorem behind Double Machine Studying
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Causal inference, and particularly causal machine studying, is an indispensable instrument that may assist us make choices by understanding trigger and impact. Optimizing costs, lowering buyer churn, working focused advert campaigns, and deciding which sufferers would profit most from medical therapy are all instance use instances for causal machine studying.
There are numerous methods for causal machine studying issues, however the method that appears to face out most is named Double Machine Studying (DML) or Debiased/Orthogonal Machine Studying. Past the empirical success of DML, this method 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 via hands-on examples. We’ll focus on the instinct for DML and empirically confirm its generality on more and more advanced examples. This text will not be a tutorial on DML, as a substitute it serves as motivation for the way DML fashions see previous mere correlation to grasp and predict trigger and impact.
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