The goal of off-policy evaluation (OPE) is to evaluate a new policy using historical data
obtained via a behavior policy. However, because the contextual bandit algorithm updates …

Off-policy evaluation (OPE) is the problem of estimating the value of a target policy from
samples obtained via different policies. Recently, applying OPE methods for bandit …

It has become increasingly common for data to be collected adaptively, for example using
contextual bandits. Historical data of this type can be used to evaluate other treatment …

Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using
data generated by a different policy. Because of its huge potential impact in practice, there …

We develop a generic data-driven method for estimator selection in off-policy policy
evaluation settings. We establish a strong performance guarantee for the method, showing …

In this work, we consider the off-policy policy evaluation problem for contextual bandits and
finite horizon reinforcement learning in the nonstationary setting. Reusing old data is critical …

We build and publicize the Open Bandit Dataset to facilitate scalable and reproducible
research on bandit algorithms. It is especially suitable for off-policy evaluation (OPE), which …

Off-policy policy evaluation is a fundamental problem in reinforcement learning. As a result,
many estimators with different tradeoffs have been developed; however, selecting the best …

Off-policy learning is a framework for evaluating and optimizing policies without deploying
them, from data collected by another policy. Real-world environments are typically non …

We study Off-Policy Evaluation (OPE) in contextual bandit settings with large action spaces.
The benchmark estimators suffer from severe bias and variance tradeoffs. Parametric …