Debiased Machine Learning and Network Cohesion for Doubly-Robust Differential Reward Models in Contextual Bandits ...
A common approach to learning mobile health (mHealth) intervention policies is linear Thompson sampling. Two desirable mHealth policy features are (1) pooling information across individuals and time and (2) incorporating a time-varying baseline reward. Previous approaches pooled information across i...
Main Authors: | , , , , , |
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Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
arXiv
2023
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Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.2312.06403 https://arxiv.org/abs/2312.06403 |
Summary: | A common approach to learning mobile health (mHealth) intervention policies is linear Thompson sampling. Two desirable mHealth policy features are (1) pooling information across individuals and time and (2) incorporating a time-varying baseline reward. Previous approaches pooled information across individuals but not time, failing to capture trends in treatment effects over time. In addition, these approaches did not explicitly model the baseline reward, which limited the ability to precisely estimate the parameters in the differential reward model. In this paper, we propose a novel Thompson sampling algorithm, termed ''DML-TS-NNR'' that leverages (1) nearest-neighbors to efficiently pool information on the differential reward function across users and time and (2) the Double Machine Learning (DML) framework to explicitly model baseline rewards and stay agnostic to the supervised learning algorithms used. By explicitly modeling baseline rewards, we obtain smaller confidence sets for the differential reward ... |
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