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Computer Science > Machine Learning

arXiv:2210.08338 (cs)
[Submitted on 15 Oct 2022]

Title:Fair Effect Attribution in Parallel Online Experiments

Authors:Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto, Yannik Stein, Matteo Ruffini, Fabian Moerchen
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Abstract:A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly in treatment and control groups. Despite a perfect randomization between different groups, simultaneous experiments can interact with each other and create a negative impact on average population outcomes such as engagement metrics. These are measured globally and monitored to protect overall user experience. Therefore, it is crucial to measure these interaction effects and attribute their overall impact in a fair way to the respective experimenters. We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values. We also provide a counterfactual perspective, that predicts shared impact based on conditional average treatment effects making use of causal inference techniques. We illustrate our approach in real world and synthetic data experiments.
Comments: Published as this https URL
Subjects: Machine Learning (cs.LG); Econometrics (econ.EM); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME)
Cite as: arXiv:2210.08338 [cs.LG]
  (or arXiv:2210.08338v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2210.08338
Journal reference: WWW '22: Companion Proceedings of the Web Conference 2022
Related DOI: https://doi.org/10.1145/3487553.3524211

Submission history

From: Alexander Buchholz [view email]
[v1] Sat, 15 Oct 2022 17:15:51 UTC (111 KB)
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