5 citations · 15 across the 5 of their papers we have counts for
5 papers
Generalized Causal Tree for Uplift Modeling
Preetam Nandy, Xiufan Yu, Wanjun Liu +3
Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment alloca…
Feedback Shaping: A Modeling Approach to Nurture Content Creation
Ye Tu, Chun Lo, Yiping Yuan +1
Social media platforms bring together content creators and content consumers through recommender systems like newsfeed. The focus of such recommender systems has thus far been prim…
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…
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…
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…