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 (covariate-balancing test and randomization test), matching, propensity score methods, sensitivity analysis, instrumental variables, regression discontinuity, difference-in-difference, synthetic control, and causal mediation analysis. Materials (recordings, slides, and lab code) for optional topics including repeated treatment (marginal structural models), nonbinary treatment (dose-response functions), and interference (contagion and spillover) may also be acquired for self-study. The seminar will provide a delicate balance of major methods and diverse applications as well as readily useful computer code. Knowledge of Stata (preferred) or R and logistic regression is required.
Agenda (Request syllabus)
Day 1: Potential Outcomes, Experiments, 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: Repeated Treatment, 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, 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 Bayesian propensity score estimators, combining difference-in-difference with matching, assessing covariate importance through treatment effect deviation, analyzing outcomes with network dependence, instrumental variable methods, causal inference with treatment diffusion, causal network analysis, etc.
Class Time
June 26-28 (8-10:30 pm, US ET), 2023 and on-demand. Zoom class with recordings available.
Participants and Reviews
Participants were from Binghamton University, CDC, Claremont Graduate University, Harvard University, Hebrew University of Jerusalem, Idaho State Board of Education, International University of Japan, National University of Singapore, National Women's Law Center, Purdue University, Quinnipiac University, Sacred Heart University, University of Chicago, University of Maryland, University of Michigan, University of Texas Medical Branch, Vanderbilt University, etc. Below are sampled reviews from past participants.
Wendy, University of Maryland
"Prof. An is very patient and thoroughly answered all my questions. This class has cleared out many confusions I have previously. It is a great and rewarding experience."
SLC, Purdue University
"It was an overview of several methods currently used. The labs and associated code were very useful!"
Trainee, National University of Singapore
"Condensed content with well-organized learning progress, can learn a lot while gradually catching up. I had a great time and thanks for providing this excellent course. "
Trainee, Hebrew University of Jerusalem
"(1) Professor An's clear explanations and availability. (2) The materials are comprehensive and well organized. Thanks very much for the great course! I learned a lot, and I really appreciate everything you have done to help us understand these important methods."
Trainee, University of Maryland
"This was a super interesting and useful seminar. Do files are very useful. Thanks for preparing them!"
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"Very clear introduction of various causal inference methods coupled with practical Stata code."
"I appreciated the breadth of methods covered in this course, as well as the extensive resources provided to support continued learning."
"I like how the class is structured. It makes it feel manageable. I think overall I got a lot from this course!"