Correlation is not causation. Counterfactual causal inference is 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, permutation 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. Examples and code will be provided. 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. Knowledge of Stata (preferred) or R and logistic regression is required.
Day 1: Experiments, Permutation 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
Dr. Weihua An is Associate Professor of Sociology & Quantitative Theory and Methods and associated faculty of 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, health, and organizations. He has published in top methodological and substantive journals and has created multiple R packages for statistical analysis. He is a recipient of the Faculty Teaching Award from Emory Sociology and has advised over 20 dissertations and multiple honors theses. Dr. An studied causal inference at Harvard with Prof. Guido Imbens (A 2021 Nobel Laureate in Economics) and Prof. Donald Rubin (a founder of modern causal inference).
August 1-3 (8pm-10:30pm, US ET), 2023. Zoom class with recordings available.
Regular tuition: $575. Early-bird tuition (register by June 1, 2023): $525. Alumni and student tuition (register by June 1, 2023): $475. To audit the first class for free, please register here. To register for the full course, please click the button below.