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  1. Causal Inference with Networked Treatment Diffusion

    Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends).
  2. Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference

    Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies.
  3. Comment: The Inferential Information Criterion from a Bayesian Point of View

    As Michael Schultz notes in his very interesting paper (this volume, pp. 52–87), standard model selection criteria, such as the Akaike information criterion (AIC; Akaike 1974), the Bayesian information criterion (BIC; Schwarz 1978), and the minimum description length principle (MDL; Rissanen 1978), are purely empirical criteria in the sense that the score a model receives does not depend on how well the model coheres with background theory. This is unsatisfying because we would like our models to be theoretically plausible, not just empirically successful.
  4. Comment: Evidence, Plausibility, and Model Selection

    In his article, Michael Schultz examines the practice of model selection in sociological research. Model selection is often carried out by means of classical hypothesis tests. A fundamental problem with this practice is that these tests do not give a measure of evidence. For example, if we test the null hypothesis β = 0 against the alternative hypothesis β ≠ 0, what is the largest p value that can be regarded as strong evidence against the null hypothesis? What is the largest p value that can be regarded as any kind of evidence against the null hypothesis?
  5. Comment: Bayes, Model Uncertainty, and Learning from Data

    The problem of model uncertainty is a fundamental applied challenge in quantitative sociology. The authors’ language of false positives is reminiscent of Bonferroni adjustments and the frequentist analysis of multiple independent comparisons, but the distinct problem of model uncertainty has been fully formalized from a Bayesian perspective.
  6. Comment: Some Challenges When Estimating the Impact of Model Uncertainty on Coefficient Instability

    I once had a colleague who knew that inequality was related to an important dependent variable. This colleague knew many other things, but I focus on inequality as an example. It was difficult for my colleague to know just how to operationalize inequality. Should it be the percentage of income held by the top 10 percent, top 5 percent, or top 1 percent of the population? Should it be based on the ratio of median black income to median white income, or should it be the log of that ratio? Should it be based on the Gini index, or perhaps the Theil index would be better?
  7. Social Networks and Educational Attainment among Adolescents Experiencing Pregnancy

    Pregnant adolescents are a population at risk for dropout and have been found to complete fewer years of education than peers. Pregnant girls’ social experience in school may be a factor in their likelihood to persist, as social integration is thought to buffer dropout risk. Pregnant teens have been found to have fewer friends than their peers, but the academic ramifications of these social differences have yet to be studied. In this study the author examines whether friendship networks are associated with the relationship between adolescent pregnancy and educational attainment.

  8. Higher Education, Bigger Networks? Differences by Family Socioeconomic Background and Network Measures

    Income or health returns linked to obtaining a college degree often are greatest for individuals who come from socioeconomically disadvantaged families. Although this importantly suggests that college lessens many forms of inequality linked to parental socioeconomic status, empirical knowledge about adult network inequality remains limited. Drawing on the 1972–2014 General Social Survey, the author finds that higher education associates on average with a greater number of nonkin and community ties.
  9. Visualizing Bring-backs

    The figure plots the number of articles that have attempted to “bring” something “back in” in the social sciences by publication year and number of citations. Andrew Abbott, taking a (pessimistic) sociology of knowledge perspective, identified this tendency—beginning with Homans’s classic article “Bringing Men Back in”—as emblematic of the tendency to rediscover old ideas in sociology. The plot shows that “bring-backs” did not become a common yearly occurrence until the mid to late 1990s but are now relatively frequent.
  10. Visualizing Stochastic Actor-based Model Microsteps

    This visualization provides a dynamic representation of the microsteps involved in modeling network and behavior change with a stochastic actor-based model. This video illustrates how (1) observed time is broken up into a series of simulated microsteps and (2) these microsteps serve as the opportunity for actors to change their network ties or behavior. The example model comes from a widely used tutorial, and we provide code to allow for adapting the visualization to one’s own model.