8 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2019★ 8 cited
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
Nathan Kallus, Masatoshi Uehara
Off-policy evaluation (OPE) in both contextual bandits and reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is of…
stat.ML2019★ 2 cited
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…
stat.ML2017
Instrument-Armed Bandits
Nathan Kallus
We extend the classic multi-armed bandit (MAB) model to the setting of noncompliance, where the arm pull is a mere instrument and the treatment applied may differ from it, which gi…