  We propose a definition for the average indirect effect of a binary treatment
in the potential outcomes model for causal inference. Our definition is
analogous to the standard definition of the average direct effect, and can be
expressed without needing to compare outcomes across multiple randomized
experiments. We show that the proposed indirect effect satisfies a universal
decomposition theorem, whereby the sum of the average direct and indirect
effects always corresponds to the average effect of a policy intervention. We
also consider a number of parametric models for interference considered by
applied researchers, and find that our (non-parametrically defined) indirect
effect remains a natural estimand when re-expressed in the context of these
models.
