Unobserved interactions between people and groups play a fundamental role in domestic and international politics. Yet, despite their importance, the vast complexity of these unobserved interactions has typically frustrated efforts to quantify them, forcing scholars to assume that the units in an analysis are independent or to study a limited range of interactions. Here, I develop a framework and machine learning model for detecting and characterizing unobserved interference dynamics using all available information: outcome, covariate, and independent variable data. Given minimal assumptions, this approach guarantees an analyst-set cap on the rate of false influence detection. It is able to satisfactorily reconstruct the influence structure of a network that was approximately measured by investigators in a school bullying experiment. I apply the method to 12 social science experiments and focus on one of these, a voter turnout intervention in the UK, as a case study. I also discuss the application of this method to the analysis of influence in observational data and in answering questions about individual-level spillovers.
The video of this talk is avaialable from here.