Counterfactual estimators enable the use of existing log data to estimate how some new
target recommendation policy would have performed, if it had been used instead of the …

Off-policy evaluation and learning (OPE/L) use offline observational data to make better
decisions, which is crucial in applications where online experimentation is limited. However …

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of
hypothetical policies leveraging only offline log data. It is particularly useful in applications …

Learning an optimal policy from offline data is notoriously challenging, which requires the
evaluation of the learning policy using data pre-collected from a static logging policy. We …

In recent years, a new line of research has taken an interventional view of recommender
systems, where recommendations are viewed as actions that the system takes to have a …

We propose diagnostics, based on control variates, to detect data quality issues in logged
bandit feedback data, which is of critical importance for accurate offline evaluation and …

We study offline policy evaluation in a setting where the target policy can take actions that
were not available when the data was logged. We analyze the bias of two popular …

Off-policy evaluation is a key component of reinforcement learning which evaluates a target
policy with offline data collected from behavior policies. It is a crucial step towards safe …

ABSTRACT Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the
performance of hypothetical policies leveraging only offline log data. It is particularly useful …