assume that each unit’s response is not affected by the treatment of any other units. Versions of this assumption are sometimes called the *stable unit treatment value assumption* [SUTVA; @rubin_estimating_1974] or a *no interference* [@cox_planning_1958] assumption. Combined with random assignment to treatment, this suffices to identify $\tau$. However, for many processes and situations of interest the units are interacting, and SUTVA becomes implausible [@aronow_estimating_2011; @sobel_randomized_2006].

Rather than substituting other strong assumptions about interference, this paper considers how we can reduce bias for the ATE through both the choice of experimental design and analysis when interactions among units occur along an observed network.[^3] The design of the experiment dictates how each vertex in the network (i.e., unit) is assigned to a condition, and the analysis says how the observed responses are combined into estimates of causal quantities of interest. We study these methods by formalizing the process of experimentation in networks, proving sufficient conditions for bias reduction through design and analysis, and running extensive simulations.

We cannot consider all possible designs and analysis, but limit this work to some relatively general methods for each. We consider experimental designs that assign clusters of vertices to the same treatment; this is *graph cluster 