Optimal Experimental Design for Staggered Rollouts

• URL: http://arxiv.org/abs/1911.03764v6
• Date: Tue, 26 Sep 2023 02:47:40 GMT
• Title: Optimal Experimental Design for Staggered Rollouts
• Authors: Ruoxuan Xiong, Susan Athey, Mohsen Bayati, Guido Imbens
• Abstract summary: We study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. We propose a new algorithm, the Precision-Guided Adaptive Experiment (PGAE) algorithm, that addresses the challenges at both the design stage and at the stage of estimating treatment effects.
• Score: 11.187415608299075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cumulative effects of the treatment. We first consider non-adaptive experiments, where all treatment assignment decisions are made prior to the start of the experiment. For this case, we show that the optimization problem is generally NP-hard, and we propose a near-optimal solution. Under this solution, the fraction entering treatment each period is initially low, then high, and finally low again. Next, we study an adaptive experimental design problem, where both the decision to continue the experiment and treatment assignment decisions are updated after each period's data is collected. For the adaptive case, we propose a new algorithm, the Precision-Guided Adaptive Experiment (PGAE) algorithm, that addresses the challenges at both the design stage and at the stage of estimating treatment effects, ensuring valid post-experiment inference accounting for the adaptive nature of the design. Using realistic settings, we demonstrate that our proposed solutions can reduce the opportunity cost of the experiments by over 50%, compared to static design benchmarks.

Related papers

• Optimal Adaptive Experimental Design for Estimating Treatment Effect [14.088972921434761]
  This paper addresses the fundamental question of determining the optimal accuracy in estimating the treatment effect. By incorporating the concept of doubly robust method into sequential experimental design, we frame the optimal estimation problem as an online bandit learning problem. Using tools and ideas from both bandit algorithm design and adaptive statistical estimation, we propose a general low switching adaptive experiment framework.
  arXiv  Detail & Related papers  (2024-10-07T23:22:51Z)
• 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)
• Choosing a Proxy Metric from Past Experiments [54.338884612982405]
  In many randomized experiments, the treatment effect of the long-term metric is often difficult or infeasible to measure. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments.
  arXiv  Detail & Related papers  (2023-09-14T17:43:02Z)
• 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)
• 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)
• DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret [59.81290762273153]
  Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions to an individual's initial features and to intermediate outcomes and features at each subsequent stage. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear.
  arXiv  Detail & Related papers  (2020-05-06T13:03:42Z)
• Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling [58.56638141701966]
  Experimenters can often acquire the knowledge about the location of the global optimum. It is unknown how to incorporate the expert prior knowledge about the global optimum into Bayesian optimization. An efficient Bayesian optimization approach has been proposed via posterior sampling on the posterior distribution of the global optimum.
  arXiv  Detail & Related papers  (2020-02-26T01:57:36Z)
• Efficient Adaptive Experimental Design for Average Treatment Effect Estimation [18.027128141189355]
  We propose an algorithm for efficient experiments with estimators constructed from dependent samples. To justify our proposed approach, we provide finite and infinite sample analyses.
  arXiv  Detail & Related papers  (2020-02-13T02:04:17Z)

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.