Events Calendar

Virtual Efficient Estimation of Treatment Effect in Online Experiments

Speaker: Dr. Congshan Zhang, Meta , Core Data Science Team at Meta

Talk Title: Efficient Estimation of Treatment Effect in Online Experiments

Abstract: Randomized controlled trials are commonly used by tech companies to draw causal conclusions on various product changes. The confidence intervals from these experiments, however, are usually too large due to reasons such as limited number of users, heavy-tailed outcome variables and small treatment effects. Improving estimation efficiency for randomized controlled trials is not only a scientifically interesting but also a practically relevant area of research. In this talk, I will go over a few prominent techniques in statistics to improve estimation efficiency. Basic techniques such as CUPED and more advanced methodologies based on ML and synthetic controls will be introduced.

Biography: Congshan Zhang is a research scientist on Core Data Science Team at Meta. Congshan is interested in various topics in statistics and econometrics including causal inference, machine learning and time series. Congshan holds Ph.D. in economics from Duke University. Before joining Meta, Congshan did research on financial econometrics, with a focus on nonparametric and semi-parametric inference using high-frequency data and on testing models of financial markets. His work appears in top journals of econometrics such as Journal of Econometrics and Annals of Applied Probability.

Host: Urbashi Mitra

More Info: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 Passcode: 586135

More Information: ECE Seminar Announcement_Nov21.docx

Location: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 P

Audiences: Everyone Is Invited

Contact: Susan Wiedem

Event Link: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 Passcode: 586135