Resources
Awesome Causal Inference [Link]
STA 640: Causal Inference by Prof. Fan Li [Link]
STA 790 (Special Topics): Bayesian Causal Inference by Prof. Fan Li [Link] [Youtube]
NIPS2022 Workshop: Causal Machine Learning for Real-World Impact [Link]
Machine Learning-based Causal Inference Tutorial [Link]
Program Evaluation for Public Service (contains Lecture Note and R codes) [Link]
Exploring Causal Inference in Observational Studies: Tutorial (R codes) [Link]
ITE inference - applications [Link]
Applied Causal Inference Powered by ML and AI [Link] [Webpage]
Susan Athey - CAUSAL INFERENCE [Link]
Resources for Machine Learning for Economists [Link]
Machine Learning-based Causal Inference Tutorial [Link]
Augmented Inverse Propensity Weighting for Randomized Experiments [Link]
Applied Causal Analysis (with R) [Link]
STAT 320: Design and Analysis of Causal Studies Dr. Kari Lock Morgan and Dr. Fan Li [Link]
Guido W. Imbens's Paper for Discussion [Link]
Causal Inference in R [Link]
Workshop on the Interface of Machine Learning and Statistical Inference [Link]
Special Issue on Causal Inference The International Journal of Biostatistics [Link]
Bayesian Causal Inference for Real World Interactive Systems [Link]
Johns Hopkins Causal Inference Working Group [Link]
Estimating Effects After Weighting [Link]
Package: BART 2.9.9 [Link]
Bénédicte Colnet's website [Link]
Causal Inference Youtube Lecture
Prof. Jae-kwang Kim from Iowa State University [Link]
Korean Lecture [Link] [Lecture Materials]
Prof. Kosuke Imai from Havard University [Link]
Prof. David Sontag from MIT [Link]
Prof. Jonas Peters from University of Copenhagen [Lecture 1] [Lecture 2] [Lecture 3] [Lecture 4] [Slide]
Prof. José R. Zubizarreta from Havard University [Link]
Prof. Stefan Wager Stanford Lecture: Machine Learning & Causal Inference [Link] [Short Version Lecture Note] [Textbook]
Prof. Brady Neal - Causal Inference [Link] [Webpage] [Textbook]
Prof. Kosuke Imai STAT 286/GOV 2003: Causal Inference with Applications [Link]
Prof. ARASH AMINI's Lectures at UCLA [Link]
Textbooks
Causal Inference in Pharmaceutical Statistics [Link]
Fundamentals of Causal Inference With R [Link]
Elements of Causal Inference Foundations and Learning Algorithms (Free downloadable) [Link]
Design of Observational Studies [Link]
A First Course in Causal Inference [Link]
Causal Inference: A Statistical Learning Approach [Link]
Lecture Note of STATS 361: Causal Inference from Stanford [Link]
Well-written Papers
Stürmer, T., Wyss, R., Glynn, R. J., & Brookhart, M. A. (2014). Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs. Journal of internal medicine, 275(6), 570-580. [Link]
Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100(469), 322-331. [Link]
Imbens, G. W. (2015). Matching methods in practice: Three examples. Journal of Human Resources, 50(2), 373-419. [Link] (Well written for simple and clear notations)
Kurz, C. F. (2022). Augmented inverse probability weighting and the double robustness property. Medical Decision Making, 42(2), 156-167. [Link] (R code is very helpful)
Datasets
Vanderbilt University Dataset Bank [Link]