32 citations · 38 across the 5 of their papers we have counts for
5 papers · 1 filter
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Nathan Kallus, Angela Zhou
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual…
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Nathan Kallus, Xiaojie Mao, Angela Zhou
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algor…
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
Nathan Kallus, Xiaojie Mao, Angela Zhou
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to ind…
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Nathan Kallus, Angela Zhou
Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historica…
Policy Evaluation and Optimization with Continuous Treatments
Nathan Kallus, Angela Zhou
We study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous, going beyond previous work on discrete treatments. Previo…