Causal Inference

Description

Correlation is not causation. Counterfactual causal inference is undoubtedly one of the most important inventions in statistics and social science research methods. Based on the potential outcomes framework, this course presents the state-of-art of causal inference methods. In-depth topics include the concept of potential outcomes, experiments, randomization test, matching, propensity score methods, sensitivity analysis, instrumental variables, regression discontinuity, difference-in-difference (and its combination with matching), synthetic control, and causal mediation analysis. Learning materials (recordings, slides, and lab code) for optional, asynchronous topics including marginal structural models (for estimating the causal effect of repeated treatment), nonbinary treatment, and interference (dependence across units) may also be acquired for self-study. The seminar will offer a good balance of major methods and diverse applications. Examples and code will be provided. Knowledge of Stata (preferred) or R and logistic regression is required.

Agenda (Request syllabus)

Day 1: Experiments, Randomization Test, Matching, and Propensity Score Methods

Day 2: Sensitivity Analysis, Instrumental Variables, and Regression Discontinuity

Day 3: Difference-in-Difference, Synthetic Control, and Causal Mediation Analysis

Optional Topics: Marginal Structure Models, Nonbinary Treatment, and Interference

Instructor

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 on smoking, adolescent 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 studied causal inference at Harvard with Prof. Guido Imbens (A 2021 Nobel Laureate in Economics), Prof. Donald Rubin (a founder of modern causal inference), and Prof. Christopher Winship (A co-author of the popular book Counterfactuals and Causal Inference: Methods and Principles for Social Research). He has researched causal inference for over 16 years and has published on many topics including methods for Bayesian propensity score analysis, combining difference-in-difference with matching, assessing covariate importance through treatment effect deviation, analyzing outcomes with network dependence, instrumental variable estimations, causal inference with treatment diffusion, causal network analysis, etc.

Class Time

June 26-28 (8-10:30 pm, US ET), 2023. Zoom class with recordings available.

Registration

Regular tuition: $549. Early-bird tuition (by May 10, 2023): $499. Student (or those with economic hardship) tuition (by May 10, 2023): $449.