Causal Network Analysis

Presenter: Weihua An

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 workshop provides a review of the popular models and methods for causal network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity) and potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data). It covers models and methods for both network formation (e.g., causal improvement of the exponential random graph models) and network effects including relational, positional, and structural effects. Lastly, this workshop will also discuss future directions for causal network analysis.