Fair Inference on Outcomes


  • Razieh Nabi Johns Hopkins University
  • Ilya Shpitser Johns Hopkins University


fair inference, mediation analysis, causal inference, algorithmic bias, constrained inference


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.




How to Cite

Nabi, R., & Shpitser, I. (2018). Fair Inference on Outcomes. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11553



AAAI Technical Track: Knowledge Representation and Reasoning