An Approach to Causal Inference Over Stochastic Networks

• Activity Number: 429 - Frequentist and Bayesian Inference for Complex Social Data
• Type: Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Duncan Clark* and Mark Stephen Handcock
• Companies: University of California, Los Angeles and University of California - Los Angeles
• Keywords: Causality; Social networks; Network models; Spillover; Contagion; Interference

Abstract

Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is unobserved and the actor covariates evolve stochastically over time. We develop a joint model for the relational and covariate generating process that avoids restrictive separability assumptions and deterministic network assumptions that do not hold in the majority of social network settings of interest. Our framework utilizes the highly general class of Exponential-family Random Network models (ERNM) of which Markov Random Fields (MRF) and Exponential-family Random Graph models (ERGM) are special cases. We present potential outcome based inference within a Bayesian framework, and propose a version of the exchange algorithm for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the framework in a case-study considering smoking behaviours for an adolescent friendship network.