Topic:

Causal Clustering: Design of Cluster Experiments Under Network Interference

Time&Date: 

10:30 am-12:00 pm, October 25, 2024 (Friday)

Venue

Online Zoom Meeting

Speaker:

Prof. Lihua Lei (Stanford University)

This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment.

Biography:

Lihua Lei is an assistant professor at Stanford Graduate School of Business and Department of Statistics (by courtesy) and Faculty Fellow at Stanford Institute for Economic Policy Research. He got his PhD in statistics at UC Berkeley and spent three years at Stanford Statistics as a postdoc. His research areas include causal inference, econometrics, experimental design, conformal inference, multiple hypothesis testing, network clustering, high-dimensional statistics, and stochastic optimization.