Causal Models for Growing Networks

Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the $\mathit{\text{distribution}}$ of edges, we propose that the relevant symmetries refer to the $\mathit{\text{causal}}\mathit{\text{structure}}$ between them. We first enumerate the 96 causal directed acyclic graph (DAG) models over pairs of nodes (dyad variables) in a growing network with finite ancestral sets that are invariant to node deletion. We then partition them into 21 classes with ancestral sets that are closed under node marginalization. Several of these classes are remarkably amenable to distributed and asynchronous evaluation. As an example, we highlight a simple model that exhibits flexible power-law degree distributions and emergent phase transitions in sparsity, which we characterize analytically. With few parameters and much conditional independence, our proposed framework provides natural baseline models for causal inference in relational data.