Conditional Average Treatment Effect CATE
Tags: #machine learning #causual inferenceEquation
$$\tau(x):=\mathbb{E}[Y(1)-Y(0)|X=x]$$Latex Code
\tau(x):=\mathbb{E}[Y(1)-Y(0)|X=x]
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Introduction
Equation
Latex Code
\tau(x):=\mathbb{E}[Y(1)-Y(0)|X=x]
Explanation
Since we can't observe ITE of item i directly, most causal inference models estimate the conditional average treatment effect(CATE) conditioned on item i (X=x_{i}).
Related Documents
- A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
- Stanford STATS 361: Causal Inference
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