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Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits

Keisuke Hirano, Jack R. Porter

arXiv 6 Feb 2023 · Econometrics · 3 citations (OpenAlex)

arXiv:2302.03117 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We develop asymptotic approximations that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and related settings. In batched adaptive settings where the decision at one stage can affect the observation of variables in later stages, our asymptotic representation characterizes all limit distributions attainable through a joint choice of an adaptive design rule and statistics applied to the adaptively generated data. This facilitates local power analysis of tests, comparison of adaptive treatments rules, and other analyses of batchwise sequential statistical decision rules.

Citation extraction

43
references
70
in-text mentions
43
distinct cited
0
self-citations
11,348
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Zhang, Janson, and Murphy (2020) Inference for Batched Bandits1.00063100%
2Hadad, Hirshberg, Zhan, Wager, and Athey (2021) Confidence Intervals for Policy Evaluation in Adaptive Experiments1.00053100%
3van der Vaart (1998) Asymptotic Statistics0.7374350%
4Hirano and Porter (2009) Asymptotics for Statistical Treatment Rules0.73732100%
5Adusumilli (2025) Optimal Tests Following Sequential Experiments0.64422100%
6Chen and Andrews (2023) Optimal Conditional Inference in Adaptive Experiments0.64422100%
7Adusumilli (2022) Risk and Optimal Policies in Bandit Experiments0.58531100%
8Fan and Glynn (2021) Diffusion Approximations for Thompson Sampling0.51121100%
9Kalvit and Zeevi (2021) A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms0.51121100%
10Manski (2004) Statistical Treatment Rules for Heterogeneous Populations0.51121100%

Showing the top 10 of 43 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Batched Adaptive Network Formation0.92843
2Adaptive Neyman Allocation0.73732
3Optimal Conditional Inference in Adaptive Experiments0.73732
4Demistifying Inference after Adaptive Experiments0.69381
5Inference for Batched Adaptive Experiments0.64422
6Continuous time asymptotic representations for adaptive experiments0.64422
7Designing persuasive experiments0.64422
8A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.40511
9Experimental Design For Causal Inference Through An Optimization Lens0.40511
10Valid Post-Contextual Bandit Inference0.40511