SLearner
Tags: #machine learning #causual inferenceEquation
$$\mu(x,w)=\mathbb{E}[Y_{i}X=x_{i},W=w] \\ \hat{\tau}(x)=\hat{\mu}(x,1)\hat{\mu}(x,0)$$Latex Code
\mu(x,w)=\mathbb{E}[Y_{i}X=x_{i},W=w] \\ \hat{\tau}(x)=\hat{\mu}(x,1)\hat{\mu}(x,0)
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Introduction
Equation
Latex Code
\mu(x,w)=\mathbb{E}[Y_{i}X=x_{i},W=w] \\ \hat{\tau}(x)=\hat{\mu}(x,1)\hat{\mu}(x,0)
Explanation
SLearner use a single machine learning estimator \mu(x,w) to estimate outcome Y directly. And the treatment assigment variable W=0,1 is treated as features of Slearner models. The CATE estimation is calculated as the difference between two outputs given the same model \mu and different inputs features of W, namely w=1 and w=0.
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