148 citations · 972 across the 92 of their papers we have counts for
11 papers · 1 filter
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…
Balanced Policy Evaluation and Learning for Right Censored Data
Owen E. Leete, Nathan Kallus, Michael G. Hudgens +2
Individualized treatment rules can lead to better health outcomes when patients have heterogeneous responses to treatment. Very few individualized treatment rule estimation methods…
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…
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
Nathan Kallus, Masatoshi Uehara
Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infe…