Unconfoundedness Assumption

Tags: #machine learning #causual inference

Equation

$$\{Y_{i}(0),Y_{i}(1)\}\perp W_{i}|X_{i}$$

Latex Code

                                 \{Y_{i}(0),Y_{i}(1)\}\perp W_{i}|X_{i}

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Introduction

Equation



Latex Code

            \{Y_{i}(0),Y_{i}(1)\}\perp W_{i}|X_{i}

Explanation

The unconfoundedness assumption or CIA (Conditional Independence assumption) assume that there are no hidden confounders between (Y(0),Y(1)) vector and treatment assignment vector W, conditioned on input X.

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