In network science, as in other applied fields, answering key questions often requires distinguishing causal mechanisms from context-dependent associations. One formalization of causality uses invariance: a relationship between a target variable and a set of covariates is causal if it remains stable across diverse environments or interventions. Originally developed for linear models, this approach has been extended to more flexible frameworks, including generalized linear models, where causality is expressed via Pearson risk invariance.

In this talk, we extend causal invariance to dynamic networks, focusing on Relational Event Models (REMs), which describe instantaneous interactions over time between social actors. Because the target —the dynamic network— and potential causal drivers are stochastic processes, dependences among them are characterized using conditional local independence. By exploiting a connection between REMs and logistic regression, we extend Pearson risk invariance to this dynamic setting, producing a causal discovery algorithm that only requires data from a single observational environment. We show how this approach is able to identify, and distinguish, important causal mechanisms in network science, such as social influence and homophily.

This is joint work with Melania Lembo and Ernst Wit (USI).