ArticleAdvances in neural information processing systems2020
Inference for Batched Bandits.
Article in Advances in neural information processing systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Adaptive Randomization in Conjoint Survey Experiments.Political analysis : an annual publication of the Methodology Section of the American Political Science Association · 2026Article
- COUNTERFACTUAL INFERENCE IN SEQUENTIAL EXPERIMENTS.Annals of statistics · 2025Article
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.Biometrics · 2024Article
- Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling.Machine learning · 2024Article
- Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference · 2023Article
- Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.PloS one · 2022Article
- Statistical Inference with M-Estimators on Adaptively Collected Data.Advances in neural information processing systems · 2021Article
- Confidence intervals for policy evaluation in adaptive experiments.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data. In this work, we develop methods for inference on data collected in batches using a bandit algorithm. We first prove that the ordinary least squares estimator (OLS), which is asymptotically normal on independently sampled data, is
Identifiers
35002190PMC8734616What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.