# Privacy Aware Experiments without Cookies

arXiv: https://arxiv.org/abs/2211.03758 · HTML: https://mlsanity.com/paper/2211.03758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [Bipartite Causal Inference with Interference](https://mlsanity.com/paper/1807.08660/) (2018)
- [Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq](https://mlsanity.com/paper/2003.13555/) (2020)
- [node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching](https://mlsanity.com/paper/1904.08572/) (2019)

## Built on by (most cited first, 1 of 1)

- [A/B testing under Interference with Partial Network Information](https://mlsanity.com/paper/2404.10547/) (2024)

## Abstract (arXiv)

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 textitthird-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.