Implementing propensity score matching with network data: the effect of the General Agreement on Tariffs and Trade on bilateral trade

Motivated by the evaluation of the causal effect of the General Agreement on Tariffs and Trade on bilateral international trade flows, we investigate the role of network structure in propensity score matching under the assumption of strong ignorability. We study the sensitivity of causal inference with respect to the presence of characteristics of the network in the set of confounders conditionally on which strong ignorability is assumed to hold. We find that estimates of the average causal effect are highly sensitive to the node level network statistics in the set of confounders. Therefore, we argue that estimates may suffer from omitted variable bias when the network information is ignored, at least in our application.