Weihua An’s research while affiliated with Emory University and other places

Past research has found that participation in extracurricular activities helps develop children's cultural capital that is crucial to both education and career successes. Previous studies have examined various determinants of extracurricular participation, but mostly focused on social class, demographics, and school characteristics. In this paper we renew the Coleman tradition by putting social capital (as measured by family structure and neighborhood cohesion) in the spotlight and studying the effect of social capital on youth participation in organized extracurricular activities. By using longitudinal data from the 2004 panel of the Survey of Income and Program Participation of representative households in the U.S. and conducting various robust statistical analyses, we provide updated results on the subject. We find that a two-parent household (especially in relative to households with cohabiting parents) and neighborhood cohesion (i.e., a set of cohesive relationships among parents in the neighborhood) both have a positive and significant association with extracurricular participation. We also find such associations vary somewhat by child's sex, age, race, and the type of extracurricular activity. We conclude that to equalize children's participation in extracurricular activities future social policies should consider interventions that target low-income families and families with single-parent or cohabiting parents, that can improve neighborhood cohesion, and that are tailored by the type of extracurricular activity.

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). In this paper, I highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I show that with accurate measures of treatment interference, we can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, treatment interference effect, and treatment effect on interference. I illustrate the methods through a case study of a social network–based smoking prevention intervention.

Causal inference is a fast-growing multidisciplinary field that has drawn extensive interests from statistical sciences and health and social sciences. In this paper, we gather comprehensive information on publications and citations in causal inference and provide a review of the field from the perspective of citation network analysis. We provide descriptive analyses by showing the most cited publications, the most prolific and the most cited authors, and structural properties of the citation network. Then we examine the citation network through exponential random graph models (ERGMs). We show that both technical aspects of the publications (e.g., publication length, time and quality) and social processes such as homophily (the tendency to cite publications in the same field or with shared authors), cumulative advantage, and transitivity (the tendency to cite references' references), matter for citations. We also provide specific analysis of citations among the top authors in the field and present a ranking and clustering of the authors. Overall, our paper reveals new insights into the landscape of the field of causal inference and may serve as a case study for analyzing citation networks in a multidisciplinary field and for fitting ERGMs on big networks.