Adaptive Experimentation When You Can't Experiment

• URL: http://arxiv.org/abs/2406.10738v1
• Date: Sat, 15 Jun 2024 20:54:48 GMT
• Title: Adaptive Experimentation When You Can't Experiment
• Authors: Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain,
• Abstract summary: 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.
• Score: 55.86593195947978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly assign users to specific control or treatment experiences either for business or practical reasons. In these settings, naively comparing treatment and control groups that may result from self-selection can lead to biased estimates of underlying treatment effects. Instead, online services can employ a properly randomized encouragement that incentivizes users toward a specific treatment. Our methodology provides online services with an adaptive experimental design approach for learning the best-performing treatment for such \textit{encouragement designs}. We consider a more general underlying model captured by a linear structural equation and formulate pure exploration linear bandits in this setting. Though pure exploration has been extensively studied in standard adaptive experimental design settings, we believe this is the first work considering a setting where noise is confounded. Elimination-style algorithms using experimental design methods in combination with a novel finite-time confidence interval on an instrumental variable style estimator are presented with sample complexity upper bounds nearly matching a minimax lower bound. Finally, experiments are conducted that demonstrate the efficacy of our approach.

Related papers

• Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach [13.208141830901845]
  We show that the standard difference-in-means estimator can lead to biased estimates due to recommender interference. We propose a "recommender choice model" that describes which item gets exposed from a pool containing both treated and control items. We show that the proposed estimator yields results comparable to the benchmark, whereas the standard difference-in-means estimator can exhibit significant bias and even produce reversed signs.
  arXiv  Detail & Related papers  (2024-06-20T14:53:26Z)
• Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials [12.682443719767763]
  We show the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT) Its application may remarkably reduce the required sample size and cost of RCTs, such as phase III clinical trials.
  arXiv  Detail & Related papers  (2024-03-05T15:48:07Z)
• 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)
• 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)
• Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches [7.390918770007728]
  Motivated by practical instances involving a handful of reallocations in which outcomes are measured in batches, we develop an adaptive-driven experimentation framework. Our main observation is that normal approximations, which are universal in statistical inference, can also guide the design of adaptive algorithms.
  arXiv  Detail & Related papers  (2023-03-21T04:17:03Z)
• Synthetically Controlled Bandits [2.8292841621378844]
  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.
  arXiv  Detail & Related papers  (2022-02-14T22:58:13Z)
• Near-optimal inference in adaptive linear regression [60.08422051718195]
  Even simple methods like least squares can exhibit non-normal behavior when data is collected in an adaptive manner. We propose a family of online debiasing estimators to correct these distributional anomalies in at least squares estimation. We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.
  arXiv  Detail & Related papers  (2021-07-05T21:05:11Z)
• Learning the Truth From Only One Side of the Story [58.65439277460011]
  We focus on generalized linear models and show that without adjusting for this sampling bias, the model may converge suboptimally or even fail to converge to the optimal solution. We propose an adaptive approach that comes with theoretical guarantees and show that it outperforms several existing methods empirically.
  arXiv  Detail & Related papers  (2020-06-08T18:20:28Z)
• Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference [73.23326654892963]
  We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs.
  arXiv  Detail & Related papers  (2020-03-02T15:21:20Z)

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.