# Fair Inference On Outcomes

## TL;DR

This paper addresses fair statistical inference for outcomes, proposing a causal framework to formalize discrimination and developing methods to learn fair outcome models through constrained optimization.

## Contribution

It introduces a causal perspective on fairness in outcome inference and offers solutions to classical inference challenges using recent causal inference techniques.

## Key findings

- Formalizes discrimination as causal effects of sensitive covariates
- Proposes constrained optimization for fair outcome modeling
- Provides workarounds for classical inference complications

## Abstract

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.