<ul><li>Fairness analyses of algorithmic decision-making should include non-binary treatment decisions.</li><li>A causal framework is proposed to measure treatment disparity and its impact on outcomes.</li><li>Loan approval datasets are analyzed to reveal potential discrimination in non-binary treatment decisions.</li><li>The framework can mitigate treatment discrimination and ensure fair decision-making processes.</li></ul>

A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination

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