ArticleAdvances in neural information processing systems2021
Post-Contextual-Bandit Inference.
Article in Advances in neural information processing systems, 2021. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- The future of large-scale experiments and their challenges in the digital era.Nature human behaviour · 2026Review
- 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
- Online learning in bandits with predicted context.Proceedings of machine learning research · 2024Article
- 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
- Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.Advances in neural information processing systems · 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
5 authors.
Funding
Abstract
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study participants and increase the chance of identifying good or even best policies. To support credible inference on novel interventions at the end of the study, nonetheless, we still want to construct valid confidence intervals on average treatment effects, subgroup effects, or value of new policies. The adaptive nature of the data collected by contextual bandit algorithms, however, makes this difficult: standard estimators are no longer asymptotically normally distributed and classic confidence intervals fail to provide correct coverage. While this has been addressed in non-contextual settings by using stabilized estimators, the contextual setting poses unique challenges that we tackle for the first time in this paper. We propose the Contextual Adaptive Doubly Robust (CADR) estimator, the first estimator for policy value that is asymptotically normal under contextual adaptive data collection. The main technical challenge in constructing CADR is designing adaptive and consistent conditional standard deviation estimators for stabilization. Extensive numerical experiments using 57 OpenML datasets demonstrate that confidence intervals based on CADR uniquely provide correct coverage.
Identifiers
35785105PMC9249103What 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.