3 citations · 7 across the 4 of their papers we have counts for
4 papers
Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
Yu Liu, Runzhe Wan, James McQueen +3
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency. Traditionally, experimenters determine AE…
Leveraging covariate adjustments at scale in online A/B testing
Lorenzo Masoero, Doug Hains, James McQueen
Companies offering web services routinely run randomized online experiments to estimate the causal impact associated with the adoption of new features and policies on key performan…
Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
Runzhe Wan, Yu Liu, James McQueen +2
With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible. Therefore, there is an inc…
A Bayesian Model for Online Activity Sample Sizes
Thomas Richardson, Yu Liu, James McQueen +1
In many contexts it is useful to predict the number of individuals in some population who will initiate a particular activity during a given period. For example, the number of user…