Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles

Aldo Gael Carranza, Sanath Kumar Krishnamurthy and Susan Athey
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Aldo Gael Carranza: Stanford U
Sanath Kumar Krishnamurthy: Stanford U

Research Papers from Stanford University, Graduate School of Business

Abstract: Contextual bandit algorithms often estimate reward models to inform decision-making. However, true rewards can contain action- independent redundancies that are not relevant for decision-making. We show it is more data- efficient to estimate any function that explains the reward differences between actions, that is, the treatment effects. Motivated by this obser- vation, building on recent work on oracle-based bandit algorithms, we provide the first reduction of contextual bandits to general-purpose hetero- geneous treatment effect estimation, and we de- sign a simple and computationally efficient algo- rithm based on this reduction. Our theoretical and experimental results demonstrate that hetero- geneous treatment effect estimation in contextual bandits offers practical advantages over reward estimation, including more efficient model esti- mation and greater flexibility to model misspeci- fication.

Date: 2023-02
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:4081

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