Individual Treatment Effect ITE
Tags: #machine learning #causual inferenceEquation
$$\text{ITE}_{i}:=Y_{i}(1)-Y_{i}(0)$$Latex Code
\text{ITE}_{i}:=Y_{i}(1)-Y_{i}(0)
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Introduction
Equation
Latex Code
\text{ITE}_{i}:=Y_{i}(1)-Y_{i}(0)
Explanation
Individual Treatment Effect(ITE) is defined as the difference between the outcome of treatment group Y_i(1) over the outcome of control group Y_i(0) of the same instance i. There exists a fundamental problem that we can't observe Y_i(1) and Y_i(0) at the same time because each instance item i can only be assigned to one experiment of control group or treatment group, but never both. So we can't observe the individual treatment effect(ITE) directly for each instance i.
Related Documents
- A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
- Stanford STATS 361: Causal Inference
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