X-Learner
Tags: #machine learning #causual inferenceEquation
$$\tilde{D}^{1}_{i}:=Y^{1}_{i}-\hat{\mu}_{0}(X^{1}_{i}),\tilde{D}^{0}_{i}:=\hat{\mu}_{1}(X^{0}_{i})-Y^{0}_{i}\\ \hat{\tau}(x)=g(x)\hat{\tau}_{0}(x) + (1-g(x))\hat{\tau}_{1}(x)$$Latex Code
\tilde{D}^{1}_{i}:=Y^{1}_{i}-\hat{\mu}_{0}(X^{1}_{i}),\tilde{D}^{0}_{i}:=\hat{\mu}_{1}(X^{0}_{i})-Y^{0}_{i}\\
\hat{\tau}(x)=g(x)\hat{\tau}_{0}(x) + (1-g(x))\hat{\tau}_{1}(x)
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Introduction
Equation
Latex Code
\tilde{D}^{1}_{i}:=Y^{1}_{i}-\hat{\mu}_{0}(X^{1}_{i}),\tilde{D}^{0}_{i}:=\hat{\mu}_{1}(X^{0}_{i})-Y^{0}_{i}\\
\hat{\tau}(x)=g(x)\hat{\tau}_{0}(x) + (1-g(x))\hat{\tau}_{1}(x)
Explanation
See this paper for more details of X-learner Metalearners for estimating heterogeneous treatment effects using machine learning
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