Fair Effect Attribution in Parallel Online Experiments

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Tác giả: Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, Fabian Moerchen, Matteo Ruffini, Yannik Stein

Ngôn ngữ: eng

Ký hiệu phân loại: 174.28 Experimentation

Thông tin xuất bản: 2022

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 195934

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.Comment: Published as https://dl.acm.org/doi/10.1145/3487553.3524211
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