T-Learner
Tags: #machine learning #causual inferenceEquation
$$\mu_{0}(x)=\mathbb{E}[Y(0)|X=x],\mu_{1}(x)=\mathbb{E}[Y(1)|X=x],\\ \hat{\tau}(x)=\hat{\mu}_{1}(x)-\hat{\mu}_{0}(x)$$Latex Code
\mu_{0}(x)=\mathbb{E}[Y(0)|X=x],\mu_{1}(x)=\mathbb{E}[Y(1)|X=x],\\
\hat{\tau}(x)=\hat{\mu}_{1}(x)-\hat{\mu}_{0}(x)
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Introduction
Equation
Latex Code
\mu_{0}(x)=\mathbb{E}[Y(0)|X=x],\mu_{1}(x)=\mathbb{E}[Y(1)|X=x],\\
\hat{\tau}(x)=\hat{\mu}_{1}(x)-\hat{\mu}_{0}(x)
Explanation
T-Learner models use two separate models to fit the dataset of control group W=0 and dateset of treatment group W=1. The CATE estimation is calculated as the difference between two outputs given same input x and different models \mu_0 and \mu_1.
Related Documents
- A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
- Stanford STATS 361: Causal Inference
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