Opening the Blackbox of Treatment Interference: Tracing Treatment Diffusion through Network Analysis

Causal inference under treatment interference is a challenging but important problem. Past studies usually make strong assumptions on the structure of treatment interference in order to estimate causal treatment effects while accounting for the effect of treatment interference. In this article, we view treatment diffusion as a concrete form of treatment interference that is prevalent in social settings and also as an outcome of central interest. Specifically, we analyze data from a smoking prevention intervention conducted with 4,094 students in six middle schools in China. We measure treatment interference by tracing how the distributed intervention brochures are shared by students, which provides information to construct the so-called treatment diffusion networks. Besides providing descriptive analyses, we use exponential random graph models to model the treatment diffusion networks in order to reveal covariates and network processes that significantly correlate with treatment diffusion. We show that the findings provide an empirical basis to evaluate previous assumptions on the structure of treatment interference, are informative for imputing treatment diffusion data that is crucial for making causal inference under treatment interference, and shed light on how to improve designs of future interventions that aim to optimize treatment diffusion.