Causal Network Analysis
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 analysis of network effects including relational, positional, and structural effects, 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). Lastly, this workshop will also discuss future directions for causal network analysis including causal improvement of the exponential random graph models for modeling network formation. Examples and R code will be provided. Knowledge of R and logistic regression is required.
Introduction to Network Analysis (Examples, history, and major topics)
Types of Network Effects (Relational, positional, and structural effects)
Major Models (The social capital model and the social contagion model)
Causal Inference Methods (Experiments, instrumental variables, and dynamic models.)
Dr. Weihua An is Associate Professor (tenured) of Sociology & Quantitative Theory and Methods and associated faculty of the East Asian Studies Program, the Goizueta Business School, and the Rollins School of Public Health at Emory University. He received a Ph.D. in Sociology and an A.M. in Statistics from Harvard University and was a doctoral fellow and a postdoc fellow at Harvard Kennedy School. His research advances theories and methods for network analysis and causal inference with applications to studying inequality and social policy (e.g., immigration, housing, education, and redistributive policy), health (e.g., peer effects, perceived risk, social network-based interventions, and pandemics), and organizations. He has published widely in both methodological and substantive journals and has also authored multiple statistical software including "fglsnet", "LARF", and "keyplayer" in R and "DIDMatch' in Stata, which have received over 130K downloads in total. He has served or is serving on the editorial boards of American Sociological Review, Journal of Machine Learning Research, Social Science Research, Sociological Methodology, and Sociological Methods and Research and has edited several special issues at top journals including the latest Methodological Advances in Quantitative Social Science (Social Science Research 50th Anniversary Series). He is a recipient of the Faculty Teaching Award from Emory Sociology and has advised over 20 dissertations and multiple honors theses. He is an instructor for the NIDA-funded program “Training in Advanced Data Analytics and Computational Sciences” at Emory and also has extensive experience in leading methods training programs for researchers and working professionals. Dr. An has studied social network analysis for over 16 years and has published on a range of topics, including causal network analysis (e.g., instrumental variable methods, social network-based interventions, and multilevel meta network analysis), network formation (e.g., theories on status differential and differential homophily), combing peer-reports and self-reports to improve data measurement, big and text network analysis (e.g., the “blocking-bridging-stacking” method), and assessing the joint effects of network and neighborhood, etc.
On-demand. Lecture slides, lab code, and recordings are available to view at your convenience.