  The assumption that data samples are independent and identically distributed
(iid) is standard in many areas of statistics and machine learning.
Nevertheless, in some settings, such as social networks, infectious disease
modeling, and reasoning with spatial and temporal data, this assumption is
false. An extensive literature exists on making causal inferences under the iid
assumption [18, 12, 28, 22], even when unobserved confounding bias may be
present. But, as pointed out in [20], causal inference in non-iid contexts is
challenging due to the presence of both unobserved confounding and data
dependence. In this paper we develop a general theory describing when causal
inferences are possible in such scenarios. We use segregated graphs [21], a
generalization of latent projection mixed graphs [30], to represent causal
models of this type and provide a complete algorithm for non-parametric
identification in these models. We then demonstrate how statistical inference
may be performed on causal parameters identified by this algorithm. In
particular, we consider cases where only a single sample is available for parts
of the model due to full interference, i.e., all units are pathwise dependent
and neighbors' treatments affect each others' outcomes [26]. We apply these
techniques to a synthetic data set which considers users sharing fake news
articles given the structure of their social network, user activity levels, and
baseline demographics and socioeconomic covariates.
