global interference. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias and error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.'
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title: 'Design and analysis of experiments in networks: Reducing bias from interference'
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Introduction
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Many situations and processes of interest to scientists involve individuals interacting with each other, such that causes of the behavior of one individual are also indirect causes of the behaviors of other individuals; that is, there are *peer effects* or *social interactions* [@manski_economic_2000]. Likewise, in applied work, the policies considered by decision-makers often have many of their effects through the interactions of individuals. Examples of such cases are abundant. In online social networks, the behavior of a single user explicitly and by design affects the experiences of other users in the network. If an experimental treatment changes a user’s behavior, then it is reasonable to expect that this will have some effect on their friends, a perhaps smaller effect on their friends 