Learning Across Experiments and Time: Tackling Heterogeneity in A/B Testing
跨实验与跨时间学习：应对 A/B 测试中的异质性AI 解读 ↓
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跨实验与跨时间学习：应对 A/B 测试中的异质性
- 研究问题
- 当实验效应随时间演化、不同实验之间存在差异时，如何选择性汇集相关信息，稳定短期 A/B 测试的处理效应估计？
- 方法与思路
- 作者提出局部经验贝叶斯框架，依据实验内时间位置与实验间情境构建可比集合，再从该集合选择性借用信息。
- 材料陈述的贡献
- 摘要称，理论分析和经验评估显示，局部汇集相较全局汇集可降低方差并避免偏差，使估计兼顾时间动态和实验情境。
- 阅读关注点
- 应核对可比实验和时间邻域的选择、经验贝叶斯参数估计、偏差控制条件，以及早停和长期指标下的不确定性处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未列出避免偏差的条件和区间覆盖结果，不能据此声称任意实验汇集均可靠。
原文摘要
A/B testing plays a central role in data-driven product development, guiding launch decisions for new features and designs. However, treatment effect estimates are often noisy due to short horizons, early stopping, and slowly accumulating long-tail metrics, making early conclusions unreliable. A natural remedy is to pool information across related experiments, but naive pooling potentially fails: within experiments, treatment effects may evolve over time, so mixing early and late outcomes without accounting for nonstationarity induces bias; across experiments, heterogeneity in product, user population, or season dilutes the signal with unrelated noise. These issues highlight the need for pooling strategies that adapt to both temporal evolution and cross-experiment variability. To address these challenges, we propose a local empirical Bayes framework that adapts to both temporal and cross-experiment heterogeneity. Throughout an experiment's timeline, our method builds a tailored comparison set: time-aware within the experiment to respect nonstationarity, and context-aware across experiments to draw only from comparable counterparts. The estimator then borrows strength selectively from this set, producing stabilized treatment effect estimates that remain sensitive to both time dynamics and experimental context. Through theoretical analysis and empirical evaluation, we show that the proposed local pooling strategy consistently outperforms global pooling by reducing variance while avoiding bias. Our proposed framework enhances the reliability of A/B testing under practical constraints, thereby enabling more timely and informed decision-making.