# Power Constrained Bandits

arXiv: https://arxiv.org/abs/2004.06230 · HTML: https://mlsanity.com/paper/2004.06230/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 30 of 30)

- [Statistical Inference with M-Estimators on Adaptively Collected Data](https://mlsanity.com/paper/2104.14074/) (2021)
- [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022)
- [Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care](https://mlsanity.com/paper/2208.07406/) (2022)
- [Bandit Algorithms for Precision Medicine](https://mlsanity.com/paper/2108.04782/) (2021)
- [Online Experimental Design With Estimation-Regret Trade-off Under Network Interference](https://mlsanity.com/paper/2412.03727/) (2024)
- [Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling](https://mlsanity.com/paper/2111.00137/) (2021)
- [Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization](https://mlsanity.com/paper/2307.02108/) (2023)
- [Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments](https://mlsanity.com/paper/2103.12198/) (2021)
- [Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health](https://mlsanity.com/paper/2310.18326/) (2023)
- [Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions](https://mlsanity.com/paper/2601.15353/) (2026)
- [Online learning in bandits with predicted context](https://mlsanity.com/paper/2307.13916/) (2023)
- [Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials](https://mlsanity.com/paper/2402.17003/) (2024)
- [CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation](https://mlsanity.com/paper/2412.08052/) (2024)
- [Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study](https://mlsanity.com/paper/2311.14359/) (2023)
- [Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services](https://mlsanity.com/paper/2408.07647/) (2024)
- [Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life](https://mlsanity.com/paper/2408.12778/) (2024)
- [Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization](https://mlsanity.com/paper/2112.08507/) (2021)
- [Entropy Regularization for Population Estimation](https://mlsanity.com/paper/2208.11747/) (2022)
- [Privacy Preserving Adaptive Experiment Design](https://mlsanity.com/paper/2401.08224/) (2024)
- [Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation](https://mlsanity.com/paper/2409.10526/) (2024)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)
- [Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics](https://mlsanity.com/paper/2511.02944/) (2025)
- [Statistical Consequences of Dueling Bandits](https://mlsanity.com/paper/2111.00870/) (2021)
- [Adaptive Interventions for Global Health: A Case Study of Malaria](https://mlsanity.com/paper/2303.02075/) (2023)
- [Adaptive Interventions with User-Defined Goals for Health Behavior Change](https://mlsanity.com/paper/2311.09483/) (2023)
- [Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings](https://mlsanity.com/paper/2408.07629/) (2024)
- [Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx](https://mlsanity.com/paper/2408.08024/) (2024)
- [Beyond ATE: Multi-Criteria Design for A/B Testing](https://mlsanity.com/paper/2509.05864/) (2025)
- [The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification](https://mlsanity.com/paper/2510.01020/) (2025)
- [Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability](https://mlsanity.com/paper/2512.09850/) (2025)

## Abstract (arXiv)

Contextual bandits often provide simple and effective personalization in decision making problems, making them popular tools to deliver personalized interventions in mobile health as well as other health applications. However, when bandits are deployed in the context of a scientific study -- e.g. a clinical trial to test if a mobile health intervention is effective -- the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective. It is essential to assess the effectiveness of the intervention before broader deployment for better resource allocation. The two objectives are often deployed under different model assumptions, making it hard to determine how achieving the personalization and statistical power affect each other. In this work, we develop general meta-algorithms to modify existing algorithms such that sufficient power is guaranteed while still improving each user's well-being. We also demonstrate that our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies, thus providing a valuable tool to study designers.