25 citations · 33 across the 5 of their papers we have counts for
5 papers
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning
Susan Athey, Undral Byambadalai, Vitor Hadad +3
We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses…
A Flexible Micro-Randomized Trial Design and Sample Size Considerations
Jing Xu, Xiaoxi Yan, Caroline Figueroa +2
Technological advancements have made it possible to deliver mobile health interventions to individuals. A novel framework that has emerged from such advancements is the just-in-tim…
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delive…
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling
Nina Deliu, Joseph J. Williams, Sofia S. Villar
Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference (e.g., biased estimators, inflated t…
Multi-Level Micro-Randomized Trial: Detecting the Proximal Effect of Messages on Physical Activity
Jing Xu, Xiaoxi Yan, Caroline Figueroa +2
Technological advancements in mobile devices have made it possible to deliver mobile health interventions to individuals. A novel intervention framework that emerges from such adva…