  Classical causal and statistical inference methods typically assume the
observed data consists of independent realizations. However, in many
applications this assumption is inappropriate due to a network of dependences
between units in the data. Methods for estimating causal effects have been
developed in the setting where the structure of dependence between units is
known exactly, but in practice there is often substantial uncertainty about the
precise network structure. This is true, for example, in trial data drawn from
vulnerable communities where social ties are difficult to query directly. In
this paper we combine techniques from the structure learning and interference
literatures in causal inference, proposing a general method for estimating
causal effects under data dependence when the structure of this dependence is
not known a priori. We demonstrate the utility of our method on synthetic
datasets which exhibit network dependence.
