12 citations · 23 across the 4 of their papers we have counts for
4 papers
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…