Licheng Liu and Xun Pang, "Bayesian Causal Inference with Presence of Interference for Longitudinal Network Data," Working Paper (2022)
This paper identifies and estimates the causal effect of an intervention on repeatedly measured units that co-exist and interact with one another in a social network, when the dichotomous intervention is not randomly assigned and the network evolution may be driven by choices of social agents. We adopt the potential outcome framework and develop identification assumptions to define and identify three estimands, namely, the direct treatment effect, the spillover effect, and the general treatment effect.  Our framework incorporates  social network ties as part of the joint treatment and treats longitudinal networks as variables rather than constants. It also considers complicated causal paths generated by interdependent outcomes. We propose a model-based estimation strategy and use a factor analysis to correct for biases caused by latent homophily. By imputing potential outcomes based on simultaneous equations, we disentangle spillover effects from direct treatment effects and explicitly estimate first-order and higher-order causal effects. The proposed method is easy to implement and flexible to accommodate a wide variety of networks.