  In this paper, we consider the problem of fair statistical inference
involving outcome variables. Examples include classification and regression
problems, and estimating treatment effects in randomized trials or
observational data. The issue of fairness arises in such problems where some
covariates or treatments are "sensitive," in the sense of having potential of
creating discrimination. In this paper, we argue that the presence of
discrimination can be formalized in a sensible way as the presence of an effect
of a sensitive covariate on the outcome along certain causal pathways, a view
which generalizes (Pearl, 2009). A fair outcome model can then be learned by
solving a constrained optimization problem. We discuss a number of
complications that arise in classical statistical inference due to this view
and provide workarounds based on recent work in causal and semi-parametric
inference.
