Nonstationary Experimental Design Under Structured Trends

• 成果类型: Article; Early Access
• 署名作者: Simchi-Levi, David; Wang, Chonghuan; Zheng, Zeyu
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Texas System; University of Texas Dallas; University of California System; University of California Berkeley
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.03329
• 发表日期: 2026
• 关键词: adaptive experimental design; NONSTATIONARY; online learning

摘要

Experimentation is increasingly used across domains such as healthcare and online platforms to inform decision making, with a central goal often being the estimation of the average treatment effect (ATE). However, in many real-world settings, treatment effects, or even the treatments themselves, evolve over time, making classical experimental designs, which assume stationarity, less effective or even misleading. This paper studies nonstationary experimental design under structured trends, addressing two key objectives: (i) accurately estimating the dynamic treatment effect and (ii) minimizing regret (e.g., revenue or welfare loss) during the experiment. We propose a flexible design framework that can be tailored to achieve Pareto-optimal tradeoff between the two objectives. We further analyze how time-varying noise levels affect this tradeoff. Additionally, we establish asymptotic normality of the estimators and show that different trend orders yield different convergence rates, enabling efficient statistical tests for the existence of high-order trends.