Online Decision Making with High-Dimensional Covariates

Abstract

Decision-makers increasingly have access to rich customer-specific data, providing an opportunity to make better, personalized service decisions. For example, in healthcare, doctors can personalize interventions based on a patient’s clinical history; in marketing, companies can target ads based on customer purchase history. However, the increased variety of potentially relevant customer data implies that an individual’s covariates may be high dimensional, which, in turn, poses statistical challenges for learning personalized decision-making policies. In “Online Decision-Making with High-Dimensional Covariates,” H. Bastani and M. Bayati introduce the LASSO Bandit, an adaptive decision-making algorithm that efficiently leverages high-dimensional user covariates by learning sparse models of decision rewards. The authors illustrate the practical relevance of such an approach by evaluating it against a personalized medication dosing problem, finding that the LASSO Bandit outperforms existing bandit methods and physicians in correctly dosing a majority of patients.