In accordance with the principle of "data minimization", many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data. In this paper, we develop a method for approximate Quantile Treatment Effect (QTE) analysis using histogram aggregation. In addition, we can also achieve formal privacy guarantees using differential privacy.

翻译：遵循"数据最小化"原则，许多互联网公司倾向于记录更少的数据。然而，这往往与A/B测试的有效性相矛盾。对于包含多次观测单元的实验，一种常见的数据最小化技术是对每个单元的数据进行聚合。然而，精确的分位数估计需要完整的观测级数据。本文提出一种基于直方图聚合的近似分位数处理效应（QTE）分析方法。此外，我们还可通过差分隐私实现形式化的隐私保护保证。