Causal clustering: design of cluster experiments under network interference
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.
Lihua Lei is an Assistant Professor of Economics at Stanford Graduate School of Business (GSB), an Assistant Professor of Statistics (by courtesy), and a Faculty Fellow at Institute for Economic Policy Research (SIEPR).
He obtained his Ph.D. in Statistics at UC Berkeley, advised by Professors Peter Bickel and Michael Jordan and worked as a postdoctoral researcher in the Statistics Department at Stanford University, advised by Professor Emmanuel Candès.
He is currently a co-organizer of the International Seminar on Selective Inference (with Will Fithian, Rina Barber and Daniel Yekutieli).