mlsanityData to 7 Oct 2026

Synthetically Controlled Bandits

arXiv 2202.07079 ↗First posted 14 Feb 2022stat.ML cs.LGcited by 8

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Abstract (arXiv)

This paper presents a new dynamic approach to experiment design in settings where, due to interference or other concerns, experimental units are coarse. `Region-split' experiments on online platforms are one example of such a setting. The cost, or regret, of experimentation is a natural concern here. Our new design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes the regret associated with experimentation at no practically meaningful loss to inferential ability. We provide theoretical guarantees characterizing the near-optimal regret of our approach, and the error rates achieved by the corresponding treatment effect estimator. Experiments on synthetic and real world data highlight the merits of our approach relative to both fixed and `switchback' designs common to such experimental settings.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 74 references that are in the map. First posted on arXiv 14 Feb 2022.

Builds on

23 of 74 references.

References · 23

  1. 2015Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challengescited by 113
  2. 2020Spectral Methods for Data Science: A Statistical Perspectivecited by 111
  3. 2019Confidence Intervals for Policy Evaluation in Adaptive Experimentscited by 95
  4. 2017Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolutioncited by 63
  5. 2020Sequential Batch Learning in Finite-Action Linear Contextual Banditscited by 63
  6. 2021Meta-Thompson Samplingcited by 62
  7. 2019Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimizationcited by 58
  8. 2019Regret Bounds for Batched Banditscited by 49
  9. 2016The State of Applied Econometrics - Causality and Policy Evaluationcited by 41
  10. 2018Synthetic Difference in Differencescited by 41
  11. 2017Accurate Inference for Adaptive Linear Modelscited by 39
  12. 2013Bandits with Switching Costs: T^{2/3} Regretcited by 38
11 more
  1. 2020Design and Analysis of Switchback Experimentscited by 37
  2. 2017Why Adaptively Collected Data Have Negative Bias and How to Correct for Itcited by 36
  3. 2017An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controlscited by 36
  4. 2021Post-Contextual-Bandit Inferencecited by 32
  5. 2019On Robustness of Principal Component Regressioncited by 27
  6. 2017Robust Synthetic Controlcited by 25
  7. 2018The Augmented Synthetic Control Methodcited by 25
  8. 2017Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCAcited by 12
  9. 2021Learning Treatment Effects in Panels with General Intervention Patternscited by 5
  10. 2021Synthetic Controls for Experimental Designcited by 5
  11. 2019mRSC: Multi-dimensional Robust Synthetic Controlcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 1; 2025: 0; 2026: 1.

8 papers.

  1. 2023On the Sublinear Regret of GP-UCBcited by 25
  2. 2022Markovian Interference in Experimentscited by 22
  3. 2022Adaptive Experimentation in the Presence of Exogenous Nonstationary Variationcited by 7
  4. 2023Adaptive Principal Component Regression with Applications to Panel Datacited by 4
  5. 2024Adaptive Experiment Design with Synthetic Controlscited by 2
  6. 2023Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oraclecited by 0
  7. 2023Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Explorationcited by 0
  8. 2026Experimentation and Commitment under Reward Shiftscited by 0