Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes
domains such as healthcare, where direct deployment is often infeasible, unethical, or …

We propose a robust regression approach to off-policy evaluation (OPE) for contextual
bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression …

Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes
domains such as healthcare, where exploration is often infeasible, unethical, or expensive …

Evaluating the value of a hypothetical target policy with only a logged dataset is important
but challenging. On the one hand, it brings opportunities for safe policy improvement under …

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 study off-policy evaluation (OPE) in the problem of slate contextual bandits where a
policy selects multi-dimensional actions known as slates. This problem is widespread in …

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 …

We study decision making in environments where the reward is only partially observed, but
can be modeled as a function of an action and an observed context. This setting, known as …

We study the off-policy evaluation problem—estimating the value of a target policy using
data collected by another policy—under the contextual bandit model. We consider the …