8 citations · 15 across the 2 of their papers we have counts for
4 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…
Efficient Policy Learning from Surrogate-Loss Classification Reductions
Andrew Bennett, Nathan Kallus
Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classi…
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…
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…