ArticleProceedings of the National Academy of Sciences of the United States of America2021
Confidence intervals for policy evaluation in adaptive experiments.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- The future of large-scale experiments and their challenges in the digital era.Nature human behaviour · 2026Review
- 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
- Effect-Invariant Mechanisms for Policy Generalization.Journal of machine learning research : JMLR · 2024Article
- Response-adaptive randomization in clinical trials: from myths to practical considerations.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023Article
- Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.Algorithms · 2022Article
- Estimating design operating characteristics in Bayesian adaptive clinical trials.The Canadian journal of statistics = Revue canadienne de statistique · 2022Article
- Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.Advances in neural information processing systems · 2021Article
- Post-Contextual-Bandit Inference.Advances in neural information processing systems · 2021Article
- Statistical Inference with M-Estimators on Adaptively Collected Data.Advances in neural information processing systems · 2021Article
- Texts don't nudge: An adaptive trial to prevent the spread of COVID-19 in India.Journal of development economics · 2021Article
- Power Constrained Bandits.Proceedings of machine learning research · 2021Article
- Inference for Batched Bandits.Advances in neural information processing systems · 2020Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Adaptive experimental designs can dramatically improve efficiency in randomized trials. But with adaptively collected data, common estimators based on sample means and inverse propensity-weighted means can be biased or heavy-tailed. This poses statistical challenges, in particular when the experimenter would like to test hypotheses about parameters that were not targeted by the data-collection mechanism. In this paper, we present a class of test statistics that can handle these challenges. Our approach is to adaptively reweight the terms of an augmented inverse propensity-weighting estimator to control the contribution of each term to the estimator's variance. This scheme reduces overall variance and yields an asymptotically normal test statistic. We validate the accuracy of the resulting estimates and their CIs in numerical experiments and show that our methods compare favorably to existing alternatives in terms of mean squared error, coverage, and CI size.
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