What is the most statistically efficient way to do off-policy optimization with batch data from bandit feedback? For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy. Our estimators are shown to have the...

What is the most statistically efficient way to do off-policy optimization with batch data from bandit feedback? For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy. Our estimators are shown to have lowest...

We examine how to learn personalized customer retention strategies when customers' intentions to purchase evolve over time. Working with a Japanese online platform, we first implement a large-scale randomized experiment, in which coupons are randomly sent to first-time buyers at different times....