Title: Causal Interventions for Fairness,
Abstract: Most approaches in algorithmic fairness constrain machine learning methods so
the resulting predictions satisfy one of several intuitive notions of fairness.
While this may help private companies comply with non-discrimination laws or
avoid negative publicity, we believe it is often too little, too late. By the
time the training data is collected, individuals in disadvantaged groups have
already suffered from discrimination and lost opportunities due to factors out
of their control. In the present work we focus instead on interventions such as
a new public policy, and in particular, how to maximize their positive effects
while improving the fairness of the overall system. We use causal methods to
model the effects of interventions, allowing for potential interference--each
individual's outcome may depend on who else receives the intervention. We
demonstrate this with an example of allocating a budget of teaching resources
using a dataset of schools in New York City.