Causal Inference

Description

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.

Agenda (Request syllabus)

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

Instructor

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 widely in both methodological and substantive journals and has created multiple R packages for data analysis including "fglsnet", "LARF", and "keyplayer". 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 journal special issues 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. 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).

Class Time

July 17-19 (8-10:30 pm, US ET), 2023. Zoom class with recordings available.

Registration

Regular tuition: $550. Early-bird tuition (by June 1, 2023): $500. Student (or those with economic hardship) tuition (by June 1, 2023): $450.

Participants and Reviews

Participants were from Hebrew University of Jerusalem, National University of Singapore, Purdue University, University of Chicago, University of Maryland, 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."