Causal Inference with Social Network Data: Inflated Effective Sample Sizes, Deflated Standard Errors, and Other Perils

• Activity Number: 110
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Elizabeth Ogburn*+
• Companies: Johns Hopkins University
• Keywords: networks ; social networks ; dependent data ; causal inference ; peer effects

Abstract

Interest in and availability of network data has sparked new work in causal and statistical inference for observations linked by network ties. This work is motivated by the Health Outcomes, Progressive Entrepreneurship, and Networks (HopeNet) Study, which will collect three waves of complete social network data and implement clean water and microenterprise interventions in a small community in southwestern Uganda. Causal effects of interest include the effects of an individual's exposure to each intervention on his own outcome, and several different types of effects of an individual's exposure on the outcomes of his social contacts. Estimation of these and other effects is challenging when only a single network of non-independent observations is observed and the dependence among observations is informed by network topology. We present analytic and simulation-based characterizations of the effective sample size for estimation of causal effects and descriptive statistics in the presence of network dependence, and discuss some new methods for estimation in this context.