Distributionally Robust Batch Contextual Bandits

Understand

Policy learning using historical observational data is an important problem that has found widespread applications.

• Examples include selecting offers, prices, advertisements to send to customers, as well as selecting which medication to prescribe to a patient.
• However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data -- an assumption that is often false or too coarse an approximation.
• In this paper, we lift this assumption and aim to learn a distributionally robust policy with incomplete observational data.