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stat.AP2019★ 1 cited
A Method for Measuring Network Effects of One-to-One Communication Features in Online A/B Tests
Guillaume Saint-Jacques, James Eric Sorenson, Nanyu Chen +1
A/B testing is an important decision making tool in product development because can provide an accurate estimate of the average treatment effect of a new features, which allows dev…
stat.AP2018
False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments
Yuxiang Xie, Nanyu Chen, Xiaolin Shi
Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies.…
stat.AP2018
Automatic Detection and Diagnosis of Biased Online Experiments
Nanyu Chen, Min Liu, Ya Xu
We have seen a massive growth of online experiments at LinkedIn, and in industry at large. It is now more important than ever to create an intelligent A/B platform that can truly d…