Offline Multi-Action Policy Learning: Generalization and Optimization

By Zhengyuan Zhou, Susan Athey, Stefan Wager. 2018

152 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Counterfactual inference for consumer choice across many product categories (2019, background): “An alternative approach is to evaluate and compare different pricing policies directly in the held-out data in a model-free way inspired by the policy learning literature (Dudik et al., 2011; Zhou et al., 2018).”
Policy Learning With Observational Data (2017, methodology): “Given this formalism, we can build on existing tools for weighted classiﬁcation to learn ˆ π ; see Zhou, Athey, and Wager (2018) for a further discussion.”