Network Dependence and Confounding by Network Structure Can Lead to Invalid Inference

• Activity Number: 526 - Novel Identification Frameworks for Causal Inference with Interference
• Type: Invited
• Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
• Sponsor: ENAR
• Author(s): Elizabeth Ogburn* and Youjin Lee
• Companies: Johns Hopkins Bloomberg School of Public Health and University of Pennsylvania
• Keywords: causal inference; replication crisis; social networks; dependent data

Abstract

We show that social network dependence can result in confounding by network structure, akin to confounding by population structure in GWAS studies, potentially contributing to replication crises across the health and social sciences. Researchers in these fields frequently sample subjects from one or a small number of communities, schools, hospitals, etc., and while many of the limitations of such convenience samples are well-known, the issue of statistical dependence due to social network ties has not previously been addressed. A paradigmatic example of this is the Framingham Heart Study (FHS). Using a statistic that we adapted to measure network dependence, we test for network dependence and for possible confounding by network structure in several of the thousands of influential papers published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious estimates of association and anticonservative uncertainty quantification due to unacknowledged network structure.