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Explicit and implicit bias clouds human judgement, leading to discriminatorytreatment of minority groups. A fundamental goal of algorithmic fairness is toavoid the pitfalls in human judgement by learning policies that improve theoverall outcomes while providing fair treatment to protected classes. In thispaper, we propose a causal framework that learns optimal intervention policiesfrom data subject to fairness constraints. We define two measures of treatmentbias and infer best treatment assignment that minimizes the bias whileoptimizing overall outcome. We demonstrate that there is a dilemma of balancingfairness and overall benefit; however, allowing preferential treatment toprotected classes in certain circumstances (affirmative action) candramatically improve the overall benefit while also preserving fairness. Weapply our framework to data containing student outcomes on standardized testsand show how it can be used to design real-world policies that fairly improvestudent test scores. Our framework provides a principled way to learn fairtreatment policies in real-world settings.

Inherent Trade-offs in the Fair Allocation of Treatments