5 citations · 15 across the 5 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
stat.AP2019★ 4 cited
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…
stat.ME2019★ 5 cited
Personalized Treatment Selection using Causal Heterogeneity
Ye Tu, Kinjal Basu, Cyrus DiCiccio +4
Randomized experimentation (also known as A/B testing or bucket testing) is widely used in the internet industry to measure the metric impact obtained by different treatment varian…
stat.ME2019★ 2 cited
A/B Testing in Dense Large-Scale Networks: Design and Inference
Preetam Nandy, Kinjal Basu, Shaunak Chatterjee +1
Design of experiments and estimation of treatment effects in large-scale networks, in the presence of strong interference, is a challenging and important problem. Most existing met…