Causal clustering: design of cluster experiments under network interference

TL;DR

This paper develops a framework for designing cluster experiments that effectively estimate treatment effects in networks with spillovers, using a novel optimization approach to minimize estimation error.

Contribution

It introduces a penalized min-cut optimization method for optimal clustering in network experiments, improving accuracy in the presence of spillovers.

Findings

Optimal clustering reduces mean-squared error in treatment effect estimation.

The method applies to large-scale network data, including Facebook's user network.

Guidelines for choosing between cluster and individual randomization are provided.

Abstract

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.