mlsanityData to 7 Oct 2026

Semi-parametric inference based on adaptively collected data

arXiv 2303.02534 ↗First posted 5 Mar 2023math.ST cs.LG stat.MEcited by 6

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Abstract (arXiv)

Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address this challenge in a semi-parametric context: estimating the parameter vector of a generalized linear regression model contaminated by a non-parametric nuisance component. We construct suitably weighted estimating equations that account for adaptivity in data collection, and provide conditions under which the associated estimates are asymptotically normal. Our results characterize the degree of "explorability" required for asymptotic normality to hold. For the simpler problem of estimating a linear functional, we provide similar guarantees under much weaker assumptions. We illustrate our general theory with concrete consequences for various problems, including standard linear bandits and sparse generalized bandits, and compare with other methods via simulation studies.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 65 references that are in the map. First posted on arXiv 5 Mar 2023.

Builds on

25 of 65 references.

References · 25

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
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13 more
  1. 2021Statistical Inference with M-Estimators on Adaptively Collected Datacited by 40
  2. 2021Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Banditscited by 40
  3. 2017Accurate Inference for Adaptive Linear Modelscited by 39
  4. 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38
  5. 2017Why Adaptively Collected Data Have Negative Bias and How to Correct for Itcited by 36
  6. 2021Post-Contextual-Bandit Inferencecited by 32
  7. 2020Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimationcited by 27
  8. 2021Near-optimal inference in adaptive linear regressioncited by 20
  9. 2022Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Carecited by 18
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  13. 2023Post Reinforcement Learning Inferencecited by 3

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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2024UCB algorithms for multi-armed bandits: Precise regret and adaptive inferencecited by 16
  2. 2024Inference with the Upper Confidence Bound Algorithmcited by 9
  3. 2023Adaptive Linear Estimating Equationscited by 6
  4. 2025Stable Thompson Sampling: Valid Inference via Variance Inflationcited by 6
  5. 2023Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inferencecited by 3
  6. 2024Online Data Collection for Efficient Semiparametric Inferencecited by 0