Novelty and Primacy: A Long-Term Estimator for Online Experiments
新奇效应与先入效应：在线实验的长期效应估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确采用双重差分估计在线实验中的用户学习及动态处理效应。
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新奇效应与先入效应：在线实验的长期效应估计
- 研究问题
- 短期在线实验中的处理效应如何因用户学习而变化，能否区分新奇效应与逐步采纳，并据此评估较长期的影响？
- 方法与思路
- 作者提出基于双重差分的观察性方法，在微软多项实验中检验和估计用户学习，并与已有实验方法比较。
- 材料陈述的贡献
- 摘要称该方法在易用性和统计功效方面有优势，同时讨论其他处理与时间交互存在时的局限，为长期效应评估提供工具。
- 阅读关注点
- 应核对双重差分的比较组、时间结构和识别假设，以及用户学习与其他动态效应的区分及长期影响外推的依据。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明识别假设和长期目标，功效优势须结合比较设计核对。
原文摘要
Online experiments are the gold standard for evaluating impact on user experience and accelerating innovation in software. However, since experiments are typically limited in duration, observed treatment effects are not always permanently stable, sometimes revealing increasing or decreasing patterns over time. There are multiple causes for a treatment effect to change over time. In this paper, we focus on a particular cause, user-learning, which is primarily associated with novelty or primacy. Novelty describes the desire to use new technology that tends to diminish over time. Primacy describes the growing engagement with technology as a result of adoption of the innovation. User-learning estimation is critical because it holds experimentation responsible for trustworthiness, empowers organizations to make better decisions by providing a long-term view of expected impact, and prevents user dissatisfaction. In this paper, we propose an observational approach, based on difference-in-differences technique to estimate user-learning at scale. We use this approach to test and estimate user-learning in many experiments at Microsoft. We compare our approach with the existing experimental method to show its benefits in terms of ease of use and higher statistical power, and to discuss its limitation in presence of other forms of treatment interaction with time.