not between, groups [e.g., @sobel_randomized_2006; @rosenbaum_interference_2007; @hudgens_toward_2008; @tchetgen_causal_2012]. Some more recent work has examined interference in networks more generally [@aronow_estimating_2011; @manski_identification_2013; @toulis_estimation_2013; @ugander_graph_2013], where this between-groups independence structure cannot be assumed.

This prior work has largely focused on assuming restrictions on the extent of interference (e.g., vertices are only affected by the number of neighbors treated) and then deriving results for designs and estimators motivated by these same assumptions. @aronow_estimating_2011 give unbiased estimators for ATEs under these assumptions and derive variance estimators.[^4] @ugander_graph_2013 show that graph clustered randomization puts more vertices in the conditions required for these estimators, such that the variance of these estimators is bounded for certain types of networks. But, as noted by @manski_identification_2013 and as we discuss in Section \[sec:implausibility\] below, the very processes expected to produce interference also make these assumptions implausible. The present work explicitly considers more realistic data generating processes that violate these restrictive assumptions. That is, in contrast to prior work, we evaluate design and analysis strategies under conditions other than those under which they have particular desirable properties (e.g., unbiasedness). Instead, we settle for reducing bias and error.[^5]

Model of experiments in networks {#sec:model}
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We consider experimentation in networks as 