Some Statistical and Data Challenges When Building Early-Stage Digital Experimentation and Measurement Capabilities
构建早期数字实验与测量能力的统计及数据挑战AI 解读 ↓
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构建早期数字实验与测量能力的统计及数据挑战
- 研究问题
- 组织为何需要建设数字实验与测量能力，运行实验需要哪些统计与数据要素，以及如何判断不同实验设计的数据使用效率？
- 方法与思路
- 论文以不确定性降低下的排序模型量化建设收益与风险，梳理实验设计和因果推断方法，并结合新增数据集、案例及设计评价框架。
- 材料陈述的贡献
- 摘要称论文提供能力建设的商业论证方法、实验基础要素的梳理、新数据集与详细案例，以及比较不同设计数据效率的评价框架。
- 阅读关注点
- 应核对排序模型如何把不确定性转化为收益与风险，数据集和案例的可复用范围，以及设计效率评价是否采用可比较的目标。
- 阅读边界
- 所给摘要来自 arXiv 预印本且自称学位论文，同行评审与期刊接收未核实。摘要未列出模型条件、数据细节及具体比较结果。
原文摘要
Digital experimentation and measurement (DEM) capabilities -- the knowledge and tools necessary to run experiments with digital products, services, or experiences and measure their impact -- are fast becoming part of the standard toolkit of digital/data-driven organisations in guiding business decisions. Many large technology companies report having mature DEM capabilities, and several businesses have been established purely to manage experiments for others. Given the growing evidence that data-driven organisations tend to outperform their non-data-driven counterparts, there has never been a greater need for organisations to build/acquire DEM capabilities to thrive in the current digital era. This thesis presents several novel approaches to statistical and data challenges for organisations building DEM capabilities. We focus on the fundamentals associated with building DEM capabilities, which lead to a richer understanding of the underlying assumptions and thus enable us to develop more appropriate capabilities. We address why one should engage in DEM by quantifying the benefits and risks of acquiring DEM capabilities. This is done using a ranking under lower uncertainty model, enabling one to construct a business case. We also examine what ingredients are necessary to run digital experiments. In addition to clarifying the existing literature around statistical tests, datasets, and methods in experimental design and causal inference, we construct an additional dataset and detailed case studies on applying state-of-the-art methods. Finally, we investigate when a digital experiment design would outperform another, leading to an evaluation framework that compares competing designs' data efficiency.