Unconfoundedness Assumption
Tags: #machine learning #causual inferenceEquation
$$\{Y_{i}(0),Y_{i}(1)\}\perp W_{i}|X_{i}$$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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