consisting of four phases: (i) [*initialization*]{}, (ii) [*treatment assignment*]{}, (iii) [*outcome generation*]{}, and (iv) [*estimation*]{}. A single run through these phases corresponds to a single instance of the experimental process. Treatment assignment embodies the experimental design, and the estimation phase embodies the analysis of the network experiment. These same phases, shown in Figure \[diagram\], are implemented in our simulations in which we instantiate this process many times.

![Model of the network experimentation process, consisting of *(i) initialization*, which generates the graph and vertex characteristics, *(ii) design*, which determines the randomization scheme, *(iii) outcome generation*, which observes or simulates behavior, and *(iv) analysis*, which constructs an estimator. We examine the bias and variance of treatment effect estimators under different design and analysis methods for varied initialization and outcome generation processes.[]{data-label="diagram"}](./process_diagram.pdf){width="\textwidth"}

Initialization
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Initialization is everything that occurs prior to the experiment. This includes network formation and the processes that produce vertex characteristics and prior behaviors. In some cases, we may regard this initialization process as random, and so wish to understand design and analysis decisions averaged over instances of this process; for example, we may wish to average over a distribution of networks that corresponds to a particular network formation model. 