Detecting Interference in Online Controlled Experiments with Increasing Allocation
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023
In this work, we introduce a widely applicable procedure to test for interference in A/B testing with increasing allocation. Our procedure can be implemented on top of an existing A/B testing platform with a separate flow and does not require a priori a specific interference mechanism.
The arXiv version is here The conference version is here
Recommended citation: Han, Kevin, Shuangning Li, Jialiang Mao, and Han Wu. “Detecting Interference in Online Controlled Experiments with Increasing Allocation.” In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 661-672. 2023.