12 citations · 28 across the 6 of their papers we have counts for
7 papers
Scalable Online Survey Framework: from Sampling to Analysis
Weitao Duan, Qian Wang, Rogier Verhulst +1
With the advancement in technology, raw event data generated by the digital world have grown tremendously. However, such data tend to be insufficient and noisy when it comes to mea…
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…
Large-Scale Online Experimentation with Quantile Metrics
Min Liu, Xiaohui Sun, Maneesh Varshney +1
Online experimentation (or A/B testing) has been widely adopted in industry as the gold standard for measuring product impacts. Despite the wide adoption, few literatures discuss A…
Using Ego-Clusters to Measure Network Effects at LinkedIn
Guillaume Saint-Jacques, Maneesh Varshney, Jeremy Simpson +1
A network effect is said to take place when a new feature not only impacts the people who receive it, but also other users of the platform, like their connections or the people who…
Causal inference from observational data: Estimating the effect of contributions on visitation frequency atLinkedIn
Iavor Bojinov, Ye Tu, Min Liu +1
Randomized experiments (A/B testings) have become the standard way for web-facing companies to guide innovation, evaluate new products, and prioritize ideas. There are times, howev…
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…