Estimating Treatment and Spillover Effects with the Ego-Cluster Experimental Design

TL;DR

This paper introduces a novel ego-cluster experimental design for estimating treatment and spillover effects in networks, addressing bias from interference and improving inference accuracy.

Contribution

It develops a new cluster-based randomization method, proposes estimators with proven properties, and introduces an ego-clustering algorithm for optimized experimental design.

Findings

The proposed estimators are consistent and asymptotically normal.

Simulation studies show improved efficiency over existing designs.

Empirical applications validate the method's practical effectiveness.

Abstract

Network interference occurs when a unit's outcome depends not only on its own treatment but also on the treatments received by connected units in the network. Experimental designs and analysis methods that ignore such interference can yield biased estimators of causal effects. In this paper, we develop a new experimental design for the estimation and inference of global treatment effect and spillover effect under a model-based framework and ego-cluster randomization. Under this design, the network is partitioned into a collection of ego-clusters, each consisting of a focal unit (the ego) and its network neighbors (the alters), with randomization conducted at the cluster level. We propose model-based estimators for the global treatment effect and spillover effect and establish their consistency and asymptotic normality, with asymptotic variances determined by the ego-cluster structure. Building on these theoretical results, we introduce an ego-clustering algorithm that sequentially selects egos and assigns alters to minimize asymptotic variances. Simulation studies and two empirical applications demonstrate that the proposed procedure yields accurate inference and efficiency improvements over existing network experimental designs.