Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual
policies using only offline logged data. Although many estimators have been developed …

Abstract The Off-Policy Evaluation (OPE) problem consists in evaluating the performance of
new policies from the data collected by another one. OPE is crucial when evaluating a new …

Off-policy evaluation (OPE) aims to estimate the return of a target policy using some pre-
collected observational data generated by a potentially different behavior policy. In many …

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 …

Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long-and
infinite-horizon settings due to diminishing overlap between behavior and target policies. In …

We consider off-policy evaluation (OPE), which evaluates the performance of a new policy
from observed data collected from previous experiments, without requiring the execution of …

Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long-and
infinite-horizon settings due to diminishing overlap between behavior and target policies. In …

Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy policy
evaluation (OPE) algorithms use historical data collected from running a previous policy to …

Off-policy policy estimators that use importance sampling (IS) can suffer from high variance
in long-horizon domains, and there has been particular excitement over new IS methods that …

We study minimax methods for off-policy evaluation (OPE) using value functions and
marginalized importance weights. Despite that they hold promises of overcoming the …