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Activity Number: 533 - Prediction and Inference in Statistical Machine Learning
Type: Invited
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #320630
Title: Statistical Inference After Adaptive Sampling for Longitudinal Data
Author(s): Kelly Wang Zhang* and Lucas Janson and Susan Murphy
Companies: Harvard University and Harvard University and Harvard University
Keywords: adaptively collected data; reinforcement learning; bandits; adaptive algorithms; causal effects; longitudinal data analysis
Abstract:

There is a great desire to use adaptive sampling methods, such as reinforcement learning and bandit algorithms, for the optimization of digital interventions in areas like mobile health and education. A major obstacle preventing more widespread use of such algorithms in practice is the lack of assurance that the resulting adaptively collected data can be used to reliably answer inferential questions, including questions about time-varying causal effects. In this work, we introduce the adaptive sandwich estimator to quantify uncertainty for Z-estimators on data collected by a large class of adaptive algorithms that learn to select actions by pooling the data of multiple users. Our approach is applicable to longitudinal data settings and in simpler settings, our results generalize those in the adaptive clinical trial literature. Furthermore, our inference method is robust to misspecification of the reward model used by the adaptive sampling algorithm. This work is motivated by our work in designing experiments in which reinforcement learning algorithms are used to select actions, yet reliable statistical inference is essential for conducting primary analyses after the trial is over.


Authors who are presenting talks have a * after their name.

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