32 citations · 46 across the 6 of their papers we have counts for
6 papers
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Andrew Bennett, Nathan Kallus, Lihong Li +1
Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings where experimentation is limited, such as education and healthcare. But, in these very sam…
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
Nathan Kallus, Masatoshi Uehara
Offline reinforcement learning, wherein one uses off-policy data logged by a fixed behavior policy to evaluate and learn new policies, is crucial in applications where experimentat…
Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus, Masatoshi Uehara
Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value. In this paper, we consider the…
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…
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
Nathan Kallus, Angela Zhou
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has cha…
Classifying Treatment Responders Under Causal Effect Monotonicity
Nathan Kallus
In the context of individual-level causal inference, we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples…