Complex Structure Discovery and Randomized Field Experiments on Large-Scale Social and Political Networks

Presented by Aaron Schein, Postdoctoral Fellow, Data Science Institute, Columbia University

Thursday, January 13, 2022
4:00 p.m. ET
Online

Social and political networks at many scales—from interpersonal networks of friends to international networks of countries—are a central theme of computational social science. Modern methods of data science that can contend with the complexity of networked data have the potential to break ground on long-standing questions of critical relevance to public policy. In this talk, I will present two lines of work on 1) estimating the causal effects of friend-to-friend mobilization in US elections, and 2) inferring complex latent structure in dyadic event data of country-to-country interactions. In the first part, I will discuss recent work using large-scale digital field experiments on the mobile app Outvote to estimate the causal effects of friend-to-friend texting on voter turnout in the 2018 and 2020 US elections. This work is among the first to rigorously assess the effectiveness of friend-to-friend “get out the vote” tactics, which political campaigns have increasingly embraced in recent elections. I will discuss the statistical challenges inherent to randomizing interactions between friends with a “light touch” design and will describe the methodology we developed to identify and precisely estimate causal effects despite these impediments. In the second part of this talk, I will discuss work on inferring complex latent structure in dyadic event data sets of international relations that contain millions of micro-records of the form “country i took action a to country j at time t”. The models we developed for this purpose blend elements of tensor decomposition and dynamical systems and are tailored to the challenging properties of high-dimensional discrete data. They reliably surface interpretable complex structure in dyadic event data while yielding tractable schemes for efficient posterior inference. At the end of the talk, I will briefly sketch a vision for the future of both lines of work.

Speaker Bio

Dr. Aaron Schein is a postdoctoral fellow in the Data Science Institute at Columbia. He received his PhD in Computer Science from UMass Amherst in 2019 and an MA in Linguistics and BA in Political Science also from UMass. His research develops statistical models and computational methods to analyze modern large-scale data in political science, sociology, and genetics, among other fields in the social and natural sciences.