## Individual Treatment Effect ITE

Tags: #machine learning #causual inference### Equation

$$\text{ITE}_{i}:=Y_{i}(1)-Y_{i}(0)$$### 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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