Speaker: Weihua An, Emory University
Abstract: Fueled by recent advances in statistical modeling and the rapid growth of network data, social network analysis has become increasingly popular in sociology and related disciplines. However, a significant amount of work in the field has been descriptive and correlational, which prevents the findings from being more rigorously translated into practices and policies. This talk provides a review of the popular models and methods for network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity), potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data), and future directions for causal network analysis. [Related Paper]
