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 …

Off-policy evaluation (OPE) is important for closing the gap between offline training and
evaluation of reinforcement learning (RL), by estimating performance and/or rank of target …

We study high-confidence off-policy evaluation in the context of infinite-horizon Markov
decision processes, where the objective is to establish a confidence interval (CI) for the …

We consider the off-policy evaluation problem of reinforcement learning using deep
convolutional neural networks. We analyze the deep fitted Q-evaluation method for …

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

A/B testing is critical for modern technological companies to evaluate the effectiveness of
newly developed products against standard baselines. This paper studies optimal designs …

A crucial task in decision-making problems is reward engineering. It is common in practice
that no obvious choice of reward function exists. Thus, a popular approach is to introduce …

Mediation analysis learns the causal effect transmitted via mediator variables between
treatments and outcomes, and receives increasing attention in various scientific domains to …

Ventilation should be assisted without asynchrony or cardiorespiratory instability during
anesthesia emergence until sufficient spontaneous ventilation is recovered. In this …

There is a growing interest in using reinforcement learning (RL) to personalize sequences of
treatments in digital health to support users in adopting healthier behaviors. Such sequential …