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date: 19 February 2019

Abstract and Keywords

Though scholars have developed an increasingly rich set of research findings regarding the structure of political networks, identifying causal associations between these networks and political outcomes of interest presents a variety of challenges. Addressing these challenges is especially important given the prominence of networks in theories of individual and collective behavior. This chapter uses the framework of the Neyman-Rubin causal model (potential outcomes framework) to discuss challenges to identification researchers face when studying how networks affect political outcomes. It then describes a set of strategies researchers can employ to address these challenges, including suggestions for best practices in the context of both observational and experimental research designs.

Keywords: network effect, causal identification, potential outcomes framework, exchangeability, noninterference, observational data, experiment, sensitivity analysis

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