Consider two brands that want to jointly test alternate web experiences for their customers with an A/B test. Such collaborative tests are today enabled using \textit{third-party cookies}, where each brand has information on the identity of visitors to another website. With the imminent elimination of third-party cookies, such A/B tests will become untenable. We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences. Our design respects the privacy of customers. We propose an estimater of the Average Treatment Effect (ATE), show that it is unbiased and theoretically compute its variance. Our demonstration describes how a marketer for a brand can design such an experiment and analyze the results. On real and simulated data, we show that the approach provides valid estimate of the ATE with low variance and is robust to the proportion of visitors overlapping across the brands.

翻译：考虑两个品牌希望共同对其客户的替代网络体验进行A/B测试。这类协作测试目前借助\textit{第三方Cookie}实现，即每个品牌均能获取另一网站访客的身份信息。随着第三方Cookie即将被淘汰，此类A/B测试将难以为继。我们提出一种两阶段实验设计，两个品牌仅需就实验的高层级聚合参数达成一致，即可测试替代体验方案。该设计尊重客户隐私。我们提出了平均处理效应（ATE）的估计量，证明其无偏性，并从理论上计算其方差。我们的演示阐述了品牌营销人员如何设计此类实验并分析结果。在真实与模拟数据上的实验表明，该方法能提供低方差的ATE有效估计，且对品牌间重叠访客比例具有鲁棒性。