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