Micro Effects on Macro Structure: Identification, Interpretation, Mediation, and Sensitivity Analysis for Model Selection

Abstract

How do social network interactions at the micro level generate novel network structures at the macro level? While recent methodological advancements have enabled the statistical analysis of micro–macro network effects, the current literature says little about the conditions sufficient to draw causal inference, how to evaluate indirect pathways that generate macro-level structures, or how to assess the sensitivity of empirical estimates to model choice. We address each of these problems in the micro effects on macro structure (MEMS) framework for micro–macro network analysis. We first report new formal results showing that the MEMS is nonparametrically identified under the conditional ignorability assumption and that, when identified, the MEMS can be interpreted under the “causes of effects” framework. We then show that the MEMS can be decomposed into direct and indirect effects to conduct mediation analysis when the interest is in multimechanistic pathways, where micro mechanisms shape macro outcomes by acting indirectly via intervening micro mechanisms. Finally, we build on these results to introduce a simple sensitivity test for the robustness of empirical estimates to model selection. We illustrate the utility of the methods in an empirical analysis of direct and indirect pathways linking in-group preference, out-group avoidance, and triadic closure to network segregation.