Percent Change Estimation in Large Scale Online Experiments

Abstract

Online experiments are a fundamental component of the development of web-facing products. Given their large user-bases, even small product improvements can have a large impact on user engagement or profits on an absolute scale. As a result, accurately estimating the relative impact of these changes is extremely important. I propose an approach based on an objective Bayesian model to improve the sensitivity of percent change estimation in A/B experiments. Leveraging pre-period information, this approach produces more robust and accurate point estimates and up to 50% tighter credible intervals than traditional methods. The R package abpackage provides an implementation of the approach.

References in corpus (3)

• Recursive Partitioning for Heterogeneous Causal Effects
• Ratios: A short guide to confidence limits and proper use
• Concise Summarization of Heterogeneous Treatment Effect Using Total Variation Regularized Regression