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Privacy Aware Experiments without Cookies

Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Viswanathan Swaminathan, Sridhar Mahadevan

Submitted on 3 November 2022

Abstract

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.

Preprint

Comment: Technical report

Subjects: Statistics - Methodology; Computer Science - Artificial Intelligence; Computer Science - Human-Computer Interaction

URL: http://arxiv.org/abs/2211.03758