Causal inference in connected populations with contagion

Abstract

Causal inference in connected populations is complicated by contagion and other real-world processes inducing dependence among outcomes. We address a gap in the literature on causal inference under contagion: while there is a growing body of work on estimating causal effects under contagion, little is known about how contagion impacts causal effects and inference. We provide insight into how contagion impacts causal effects and inference based on closed-form expressions for causal effects under contagion. These closed-form expressions reveal that the effects of interventions, spillover, and contagion are intertwined even in the simplest possible settings, and that contagion can decrease or increase causal effects. We discuss statistical implications, including asymptotic bias of model-based estimators ignoring dependence among outcomes due to contagion, violations of neighborhood exposure assumptions underlying design-based estimators by unrestricted contagion, and possible remedies.