通过样本分割评估A/B测试方法：理论与实践
Evaluating A/B Testing Methodologies via Sample Splitting: Theory and Practice
AI总结：
本文通过样本分割方法评估A/B测试的理论与实践，探讨样本分割估计器的偏差与方差权衡，提供模拟验证和实现指导。
摘要：
我们开发了一个理论框架，用于A/B测试环境中样本分割的理论分析，其中每个测试的数据被分成两部分，以在真实影响不可观测时测量方法性能。我们证明样本分割估计器通常对整体样本性能有偏见，但能一致地估计样本分割版本的性能。我们推导了其渐近分布，构建了有效的置信区间，并刻画了样本分割设计选择背后的偏差-方差权衡。我们通过模拟验证了理论结果，并为A/B测试产品提供评估新估计器和决策规则的实现指导。
英文摘要：
We develop a theoretical framework for sample splitting in A/B testing environments, where data for each test are partitioned into two splits to measure methodological performance when the true impacts of tests are unobserved. We show that sample-split estimators are generally biased for full-sample performance but consistently estimate sample-split analogues of it. We derive their asymptotic distributions, construct valid confidence intervals, and characterize the bias-variance trade-offs underlying sample-split design choices. We validate our theoretical results through simulations and provide implementation guidance for A/B testing products seeking to evaluate new estimators and decision rules.
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。
