A/B testing, a widely used form of Randomized Controlled Trial (RCT), is a fundamental tool in business data analysis and experimental design. However, despite its intent to maintain randomness, A/B testing often faces challenges that compromise this randomness, leading to significant limitations in practice. In this study, we introduce Bootstrap Matching, an innovative approach that integrates Bootstrap resampling, Matching techniques, and high-dimensional hypothesis testing to address the shortcomings of A/B tests when true randomization is not achieved. Unlike traditional methods such as Difference-in-Differences (DID) and Propensity Score Matching (PSM), Bootstrap Matching is tailored for large-scale datasets, offering enhanced robustness and computational efficiency. We illustrate the effectiveness of this methodology through a real-world application in online advertising and further discuss its potential applications in digital marketing, empirical economics, clinical trials, and high-dimensional bioinformatics.

翻译：A/B测试作为随机对照试验（RCT）的广泛应用形式，是商业数据分析和实验设计的基础工具。然而，尽管其旨在保持随机性，A/B测试在实践中常面临破坏随机性的挑战，导致显著的局限性。本研究提出自助匹配法，这是一种整合自助重抽样、匹配技术与高维假设检验的创新方法，旨在解决真实随机化未能实现时A/B测试的缺陷。与双重差分法（DID）和倾向得分匹配（PSM）等传统方法不同，自助匹配法专为大规模数据集设计，具有更强的稳健性和计算效率。我们通过在线广告的实际应用案例展示了该方法的有效性，并进一步探讨了其在数字营销、实证经济学、临床试验以及高维生物信息学领域的潜在应用前景。