mlsanityData to 7 Oct 2026

Privacy Preserving Adaptive Experiment Design

arXiv 2401.08224 ↗First posted 16 Jan 2024stat.ME cs.CR cs.LGcited by 1

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Abstract (arXiv)

Adaptive experiment is widely adopted to estimate conditional average treatment effect (CATE) in clinical trials and many other scenarios. While the primary goal in experiment is to maximize estimation accuracy, due to the imperative of social welfare, it's also crucial to provide treatment with superior outcomes to patients, which is measured by regret in contextual bandit framework. These two objectives often lead to contrast optimal allocation mechanism. Furthermore, privacy concerns arise in clinical scenarios containing sensitive data like patients health records. Therefore, it's essential for the treatment allocation mechanism to incorporate robust privacy protection measures. In this paper, we investigate the tradeoff between loss of social welfare and statistical power in contextual bandit experiment. We propose a matched upper and lower bound for the multi-objective optimization problem, and then adopt the concept of Pareto optimality to mathematically characterize the optimality condition. Furthermore, we propose differentially private algorithms which still matches the lower bound, showing that privacy is "almost free". Additionally, we derive the asymptotic normality of the estimator, which is essential in statistical inference and hypothesis testing.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 16 Jan 2024.

Builds on

32 of 50 references.

References · 32

  1. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  4. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  5. 2018Differentially Private Contextual Linear Banditscited by 82
  6. 2020Locally Differentially Private (Contextual) Bandits Learningcited by 52
  7. 2015Algorithms for Differentially Private Multi-Armed Banditscited by 44
  8. 2019An Optimal Private Stochastic-MAB Algorithm Based on an Optimal Private Stopping Rulecited by 37
  9. 2020Multi-Armed Bandits with Local Differential Privacycited by 37
  10. 2020Design and Analysis of Switchback Experimentscited by 37
  11. 2017Why Adaptively Collected Data Have Negative Bias and How to Correct for Itcited by 36
  12. 2020Power Constrained Banditscited by 30
20 more
  1. 2020A Primer on Private Statisticscited by 30
  2. 2021Generalized Linear Bandits with Local Differential Privacycited by 29
  3. 2022When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Banditscited by 28
  4. 2022Shuffle Private Linear Contextual Banditscited by 27
  5. 2021Differentially Private Multi-Armed Bandits in the Shuffle Modelcited by 24
  6. 2020Privacy-Preserving Dynamic Personalized Pricing with Demand Learningcited by 23
  7. 2020Efficient Adaptive Experimental Design for Average Treatment Effect Estimationcited by 22
  8. 2018Mitigating Bias in Adaptive Data Gathering via Differential Privacycited by 21
  9. 2015Always Valid Inference: Bringing Sequential Analysis to A/B Testingcited by 20
  10. 2022Differentially Private Stochastic Linear Bandits: (Almost) for Freecited by 19
  11. 2021Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacycited by 18
  12. 2017A framework for Multi-A(rmed)/B(andit) testing with online FDR controlcited by 17
  13. 2023Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experimentscited by 16
  14. 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
  15. 2019Optimal Experimental Design for Staggered Rolloutscited by 13
  16. 2021Differential Privacy in Personalized Pricing with Nonparametric Demand Modelscited by 13
  17. 2015Private Causal Inferencecited by 10
  18. 2020Panel Experiments and Dynamic Causal Effects: A Finite Population Perspectivecited by 8
  19. 2023Adaptive Neyman Allocationcited by 7
  20. 2020Privacy-aware identificationcited by 4

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Auditing Differential Privacy in the Black-Box Settingcited by 0