Synthetically Controlled Bandits

• URL: http://arxiv.org/abs/2202.07079v1
• Date: Mon, 14 Feb 2022 22:58:13 GMT
• Title: Synthetically Controlled Bandits
• Authors: Vivek Farias, Ciamac Moallemi, Tianyi Peng, Andrew Zheng
• Abstract summary: This paper presents a new dynamic approach to experiment design in settings where, due to interference or other concerns, experimental units are coarse. Our new design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes the regret associated with experimentation at no practically meaningful loss to inferential ability.
• Score: 2.8292841621378844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Adaptive Experimentation When You Can't Experiment [55.86593195947978]
  This paper introduces the emphconfounded pure exploration transductive linear bandit (textttCPET-LB) problem. Online services can employ a properly randomized encouragement that incentivizes users toward a specific treatment.
  arXiv  Detail & Related papers  (2024-06-15T20:54:48Z)
• Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification [9.030753181146176]
  We propose a unified model that simultaneously accounts for within-experiment performance and post-experiment outcomes. We show that substantial reductions in experiment duration can often be achieved with minimal impact on both within-experiment and post-experiment regret.
  arXiv  Detail & Related papers  (2024-02-16T11:27:48Z)
• Adaptive Instrument Design for Indirect Experiments [48.815194906471405]
  Unlike RCTs, indirect experiments estimate treatment effects by leveragingconditional instrumental variables. In this paper we take the initial steps towards enhancing sample efficiency for indirect experiments by adaptively designing a data collection policy. Our main contribution is a practical computational procedure that utilizes influence functions to search for an optimal data collection policy.
  arXiv  Detail & Related papers  (2023-12-05T02:38:04Z)
• ExPT: Synthetic Pretraining for Few-Shot Experimental Design [33.5918976228562]
  Experiment Pretrained Transformers (ExPT) is a foundation model for few-shot experimental design. ExPT employs a novel combination of synthetic pretraining with in-context learning. We evaluate ExPT on few-shot experimental design in challenging domains.
  arXiv  Detail & Related papers  (2023-10-30T19:25:43Z)
• A Double Machine Learning Approach to Combining Experimental and Observational Data [59.29868677652324]
  We propose a double machine learning approach to combine experimental and observational studies. Our framework tests for violations of external validity and ignorability under milder assumptions.
  arXiv  Detail & Related papers  (2023-07-04T02:53:11Z)
• Task-specific experimental design for treatment effect estimation [59.879567967089145]
  Large randomised trials (RCTs) are the standard for causal inference. Recent work has proposed more sample-efficient alternatives to RCTs, but these are not adaptable to the downstream application for which the causal effect is sought. We develop a task-specific approach to experimental design and derive sampling strategies customised to particular downstream applications.
  arXiv  Detail & Related papers  (2023-06-08T18:10:37Z)
• Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation [54.72195809248172]
  We present a new estimator leveraging our proposed novel concept, that involves retrospective reshuffling of participants across experimental arms at the end of an RCT. We prove theoretically that such an estimator is more accurate than common estimators based on sample means.
  arXiv  Detail & Related papers  (2023-02-06T05:17:22Z)
• Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls [16.449993388646277]
  We develop a globally convergent and practically efficient optimization algorithm. We establish the first global optimality guarantee for experiment design when pre-treatment data is sampled from certain data-generating processes.
  arXiv  Detail & Related papers  (2022-11-28T11:45:54Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Active Learning of Continuous-time Bayesian Networks through Interventions [38.728001040001615]
  We consider the problem of learning structures and parameters of Continuous-time Bayesian Networks (CTBNs) from time-course data under minimal experimental resources. We propose a novel criterion for experimental design based on a variational approximation of the expected information gain.
  arXiv  Detail & Related papers  (2021-05-31T07:13:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.