3 papers
cs.LG2020
Minimizing Interference and Selection Bias in Network Experiment Design
Zahra Fatemi, Elena Zheleva
Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to…
cs.IR2020
Correcting for Selection Bias in Learning-to-rank Systems
Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang +2
Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems. However, these dat…
cs.LG2019
Learning Triggers for Heterogeneous Treatment Effects
Christopher Tran, Elena Zheleva
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differ…