Replication Data for: Contagion, Confounding, and Causality: Confronting the Three C's of Observational Political Networks Research

Contagion across various types of connections is a central process in the study of many political phenomena (e.g., democratization, civil conflict, voter turnout). Over the last decade the methodological literature addressing the challenges in causally identifying contagion in networks has exploded. In one of the foundational works in this literature, ShaliziThomas2011 propose a permutation test for contagion in longitudinal network data that is not confounded by selection (e.g., homophily). We illustrate the properties of this test via simulation. We assess its statistical power under various conditions of the data; including the nature of the contagion, the structure of the network through which contagion occurs, and the number of time periods included in the data. We then apply this test to an example domain that is commonly considered in the context of observational research on contagion---the international spread of democracy. We find evidence of the international contagion of democracy. We conclude with a discussion of the practical applicability of the Shalizi & Thomas test to the study of contagion in political networks. The repository contains the R code and the Polity dataset used to produce the simulations and the Polity application results presented in the paper. It also contains the RDS objects of the simulated data and the plots produced from them.