Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Panel Experiments
基于实验设计的置信序列：面板实验中的风险缓解AI 解读 ↓
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基于实验设计的置信序列：面板实验中的风险缓解
- 研究问题
- 在随时间推进的面板和切换实验中，如何允许持续查看结果并适时停止，同时控制反复检验错误并衡量参与者承受的风险？
- 方法与思路
- 作者采用基于随机化设计的推断，以参与者层面的有限样本估计目标构造置信序列，并结合协变量和模型降低方差。
- 材料陈述的贡献
- 摘要称，方法将随时有效推断扩展至时间序列、切换及面板实验，并通过模拟和 Netflix 应用展示提前停止的潜力。
- 阅读关注点
- 核对随机化与跨期处理结构、有限样本目标的定义，以及置信序列保证哪些是渐近成立、哪些能在有限样本中成立。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要注明结果涉及渐近保证，不能将具体提前停止案例视为普遍保证。
原文摘要
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. Beyond aiding managerial decision-making, experiments mitigate risk by limiting the proportion of customers exposed to innovations. Since many experiments are conducted sequentially over time, an emerging strategy to further derisk the process is to allow managers to ``peek'' at the results as new data become available and stop the test if the results are statistically significant. The class of statistical methods that allow managers to peek and still provide valid inference are often called anytime-valid since they maintain proper uniform type-1 error guarantees. In this paper, we extend existing anytime-valid approaches to accommodate the more complex yet standard settings in time series, switchback, and panel experiments. To achieve this, we leverage the design-based approach to focus on assumption-light and managerial relevant finite-sample estimands defined on the study participants as a direct measure of the risks incurred by companies. As a special case, our (asymptotic) results also provide a robust method for achieving always-valid inference in A/B tests. We further provide a variance reduction technique incorporating modeling assumptions and covariates. Finally, we demonstrate the effectiveness of our proposed approach through a simulation study and three real-world applications from Netflix. Our results show that using our confidence sequence, harmful experiments could be stopped after only observing a handful of units; for instance, our method would have stopped a 30,000 person Netflix experiment after the first 100 people.