Primary Submission Category: Causal Inference in Networks
Design-based weighted regression estimators for conditional spillover effects
Authors: Fei Fang, Edoardo Airoldi, Laura Forastiere,
Presenting Author: Fei Fang*
In a clustered interference setting, with networks collected within clusters and no interference between clusters, we introduce a general causal estimand for conditional spillover effects, offering flexible ways of integrating unit-to-unit spillover effects. In particular, we define spillover effects from the treatment received by one neighbor, averaged over the distribution of the cluster treatment, and conditional on the characteristics of the treated unit. Such definition enables to access the heterogeneity of a unit’s spillover effect on their neighbors with respect to the unit’s characteristics. Two weighted regression-based estimators are proposed: i) at the individual level, taking neighbors’ averages either in the outcomes or in the treatments within weights; and ii) at the dyadic level, where the outcome of one unit is regressed on the treatment of each neighbor. When covariates driving the heterogeneity are categorical, we prove the equivalence of the two regression-based estimators to the non-parametric Hajek estimator. For continuous covariates, we demonstrate that both estimators consistently estimate the proposed estimands. Under a design-based perspective, we derive HAC variance estimators and establish the central limit theorem. Simulations are conducted to compare the performance of our estimators. Finally, we apply our methods to a randomized experiment conducted in Honduras to evaluate the spillover effect of a behavioral intervention.