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 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).

Class Time

August 1-3 (8pm-10:30pm, US ET), 2023. Zoom class with recordings available.

Registration

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.

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 A

"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 B

"(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."