6 citations · 22 across the 5 of their papers we have counts for
5 papers
Causal Estimation of Position Bias in Recommender Systems Using Marketplace Instruments
Rina Friedberg, Karthik Rajkumar, Jialiang Mao +3
Information retrieval systems, such as online marketplaces, news feeds, and search engines, are ubiquitous in today's digital society. They facilitate information discovery by rank…
Trustworthy Online Marketplace Experimentation with Budget-split Design
Min Liu, Jialiang Mao, Kang Kang
Online experimentation, also known as A/B testing, is the gold standard for measuring product impacts and making business decisions in the tech industry. The validity and utility o…
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…
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…