Fair Inference On Outcomes

Understand

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).