18 citations · 25 across the 4 of their papers we have counts for
4 papers
Sales Channel Optimization via Simulations Based on Observational Data with Delayed Rewards: A Case Study at LinkedIn
Diana M. Negoescu, Pasha Khosravi, Shadow Zhao +3
Training models on data obtained from randomized experiments is ideal for making good decisions. However, randomized experiments are often time-consuming, expensive, risky, infeasi…
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…
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.…
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…