| Name of Speaker | Zhao Jinglong |
| Schedule | 12 January 2024, 10am – 11.30am |
| Venue | BIZ1-0203 |
| Link to Register
|
https://nus-sg.zoom.us/meeting/register/tZEpcOGopz4jE91kG6vFGQ78zjTCRdz9iGFZ |
| Title | Adaptive Neyman Allocation |
| Abstract | In experimental design, Neyman allocation refers to the practice of allocating subjects into treated and control groups, potentially in unequal numbers proportional to their respective standard deviations, with the objective of minimizing the variance of the treatment effect estimator. This widely recognized approach increases statistical power in scenarios where the treated and control groups have different standard deviations, as is often the case in social experiments, clinical trials, marketing research, and online A/B testing. However, Neyman allocation cannot be implemented unless the standard deviations are known in advance. Fortunately, the multi-stage nature of the aforementioned applications allows the use of earlier stage observations to estimate the standard deviations, which further guide allocation decisions in later stages. In this paper, we introduce a competitive analysis framework to study this multi-stage experimental design problem. We propose a simple adaptive Neyman allocation algorithm, which almost matches the information-theoretic limit of conducting experiments. Using online A/B testing data from a social media site, we demonstrate the effectiveness of our adaptive Neyman allocation algorithm, highlighting its practicality especially when applied with only a limited number of stages. |
| About the Speaker | Jinglong Zhao is an Assistant Professor of Operations and Supply Chain Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology. |
Menu
