If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize, let alone execute. In this article, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the “no interference” assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.

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Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test statistic based on a sum of squared residuals and incorporating information about the fixed network for the simple Kolmogorov–Smirnov test statistic (Hollander 1999, section 5.4) they used. This note incrementally improves the application of BFP's “reasoning about interference” approach. We do not offer general results about test statistics for multi-parameter causal models on social networks here, but instead hope to stimulate further, and deeper, work on test statistics and sharp hypothesis testing.

To understand the relationship between place and politics, we must measure both political attitudes and the ways in which place is represented in the minds of individuals. In this paper, we assess a new measure of mental representation of geography, in which survey respondents draw their own local communities on maps and describe them. We use a panel study in Canada to present evidence that these maps are both valid and reliable measures of a personally relevant geographic area, laying the measurement groundwork for the growing number of studies using this technology. We hope to set efforts to measure “place” for the study of context and politics on firmer footing. Our validity assessments show that individuals are thinking about people and places with which they have regular contact when asked to draw their communities. Our reliability assessments show that people can draw more or less the same map twice, even when the exercise is repeated months later. Finally, we provide evidence that the concept of community is a tangible consideration in the minds of ordinary citizens and is not simply a normative aspiration or motivation.

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