Privacy Aware Experiments without Cookies

TL;DR

This paper introduces a privacy-preserving two-stage experimental design for collaborative A/B testing between brands that does not rely on third-party cookies, ensuring customer privacy while accurately estimating treatment effects.

Contribution

The authors propose a novel two-stage experimental method that enables collaborative A/B testing without third-party cookies, including an unbiased estimator for the Average Treatment Effect and variance analysis.

Findings

Provides valid ATE estimates with low variance

Robust to overlapping visitors across brands

Works on real and simulated data

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.