148 citations · 972 across the 92 of their papers we have counts for
7 papers · 2 filters
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient e…
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning
Nathan Kallus, Masatoshi Uehara
Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long- and infinite-horizon settings due to diminishing overlap between behavior and target policie…
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
Yichun Hu, Nathan Kallus, Xiaojie Mao
We study a nonparametric contextual bandit problem where the expected reward functions belong to a Hölder class with smoothness parameter . We show how this interpolates between…
Policy Evaluation with Latent Confounders via Optimal Balance
Andrew Bennett, Nathan Kallus
Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine. But it usually relies on the assumption of n…
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…
Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett, Nathan Kallus, Tobias Schnabel
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard metho…