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Offline Multi-Action Policy Learning: Generalization and Optimization

Zhengyuan Zhou¹; Susan Athey²; Stefan Wager²

¹ Stern School of Business, New York University · ² Graduate School of Business, Stanford University

As a result of digitization of the economy, more and more decision makers from a wide range of domains have gained the ability to target products, services, and information provision based on individual characteristics. Examples include selecting offers, prices, advertisements, or emails to send to consumers, choosing a bid to submit in a contextual first-price auctions, and determining which medication to prescribe to a patient. The key to enabling this is to learn a treatment policy from historical observational data in a sample-efficient way, hence uncovering the best personalized treatment choice recommendation. In “Offline Policy Learning: Generalization and Optimization,” Z. Zhou, S. Athey, and S. Wager provide a sample-optimal policy learning algorithm that is computationally efficient and that learns a tree-based treatment policy from observational data. In our quest toward fully automated personalization, the work provides a theoretically sound and practically implementable approach.

DOI

10.1287/opre.2022.2271

Volume

71 (1)

Pages

148-183

Language

en

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BibTeX

Sources

crossref