  Claiming causal inferences in network settings necessitates careful
consideration of the often complex dependency between outcomes for actors. Of
particular importance are treatment spillover or outcome interference effects.
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 simple modification to the exchange algorithm to allow for sampling
from ERNM posteriors. We present results of a simulation study demonstrating
the validity of the approach. Finally, we demonstrate the value of the
framework in a case-study of smoking over time in the context of adolescent
friendship networks.
