Optimizing Adaptive Experiments: A Unified Approach to Regret
Minimization and Best-Arm Identification
- URL: http://arxiv.org/abs/2402.10592v1
- Date: Fri, 16 Feb 2024 11:27:48 GMT
- Title: Optimizing Adaptive Experiments: A Unified Approach to Regret
Minimization and Best-Arm Identification
- Authors: Chao Qin, Daniel Russo
- Abstract summary: This paper proposes a unified model that accounts for both within-experiment performance and post-experiment outcomes.
We then provide a theory of optimal performance in large populations that unifies canonical results in the literature.
- Score: 10.66863856524397
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Practitioners conducting adaptive experiments often encounter two competing
priorities: reducing the cost of experimentation by effectively assigning
treatments during the experiment itself, and gathering information swiftly to
conclude the experiment and implement a treatment across the population.
Currently, the literature is divided, with studies on regret minimization
addressing the former priority in isolation, and research on best-arm
identification focusing solely on the latter. This paper proposes a unified
model that accounts for both within-experiment performance and post-experiment
outcomes. We then provide a sharp theory of optimal performance in large
populations that unifies canonical results in the literature. This unification
also uncovers novel insights. For example, the theory reveals that familiar
algorithms, like the recently proposed top-two Thompson sampling algorithm, can
be adapted to optimize a broad class of objectives by simply adjusting a single
scalar parameter. In addition, the theory reveals that enormous reductions in
experiment duration can sometimes be achieved with minimal impact on both
within-experiment and post-experiment regret.
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) - Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices [7.21848268647674]
This study designs an adaptive experiment for efficiently estimating average treatment effects (ATEs)
In each round of our adaptive experiment, an experimenter samples an experimental unit, assigns a treatment, and observes the corresponding outcome immediately.
At the end of the experiment, the experimenter estimates an ATE using the gathered samples.
arXiv Detail & Related papers (2024-03-06T10:24:44Z) - Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches [13.504353263032359]
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency.
Traditionally, experimenters determine AES based on domain knowledge, but this method becomes impractical for online experimentation services managing numerous experiments.
We propose two solutions for data-driven AES selection in for online experimentation services.
arXiv Detail & Related papers (2023-12-20T09:34:28Z) - DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment
Design [61.48963555382729]
We propose DiscoBAX as a sample-efficient method for maximizing the rate of significant discoveries per experiment.
We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation.
arXiv Detail & Related papers (2023-12-07T06:05:39Z) - 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) - Adaptive Identification of Populations with Treatment Benefit in
Clinical Trials: Machine Learning Challenges and Solutions [78.31410227443102]
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
We propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction.
arXiv Detail & Related papers (2022-08-11T14:27:49Z) - Policy design in experiments with unknown interference [0.0]
We study estimation and inference on policies with spillover effects.
Units are organized into a finite number of large clusters.
We provide strong theoretical guarantees and an implementation in a large-scale field experiment.
arXiv Detail & Related papers (2020-11-16T18:58:54Z) - 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) - Optimal Experimental Design for Staggered Rollouts [11.187415608299075]
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.
arXiv Detail & Related papers (2019-11-09T19:46:29Z)
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.