在线对照实验中的统计挑战：A/B测试方法综述
Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology
AI总结：
本文综述了大规模在线对照实验（OCEs）中需要新统计方法解决的挑战，讨论了在线实验的实践、文化及相关文献，旨在促进学术界与在线行业的合作。
摘要：
20世纪90年代末基于互联网的服务和产品的兴起，为在线企业参与大规模数据驱动决策带来了前所未有的机遇。在过去二十年中，Airbnb、Alibaba、Amazon、Baidu、Booking、Alphabet's Google、LinkedIn、Lyft、Meta's Facebook、Microsoft、Netflix、Twitter、Uber和Yandex等组织投入了大量资源用于在线对照实验（OCEs），以评估创新对其客户和业务的影响。大规模运行OCEs带来了诸多挑战，需要来自多个领域的解决方案。在本文中，我们回顾了需要新统计方法来解决的挑战。具体而言，我们讨论了在线实验的实践与文化及其统计学文献，将当前的方法置于其相关的统计学谱系中，并提供了OCE应用的说明性示例。我们的目标是提高学术统计学家对这些新研究机会的认识，以促进学术界与在线行业之间的合作。
英文摘要：
The rise of internet-based services and products in the late 1990's brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the past two decades, organizations such as Airbnb, Alibaba, Amazon, Baidu, Booking, Alphabet's Google, LinkedIn, Lyft, Meta's Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have invested tremendous resources in online controlled experiments (OCEs) to assess the impact of innovation on their customers and businesses. Running OCEs at scale has presented a host of challenges requiring solutions from many domains. In this paper we review challenges that require new statistical methodologies to address them. In particular, we discuss the practice and culture of online experimentation, as well as its statistics literature, placing the current methodologies within their relevant statistical lineages and providing illustrative examples of OCE applications. Our goal is to raise academic statisticians' awareness of these new research opportunities to increase collaboration between academia and the online industry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
