STA 640: Causal Inference
Fan Li
Department of Statistical Science, Duke University
Meeting times The class will next offered Spring 2022.
Instructor Fan Li, Associate Professor, Statistical Science, Email: fl35@duke.edu.
Teaching Assistant TBD
Textbooks No specific textbook, mostly based on the lecture notes and many papers. The first few lectures will loosely follow the book Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Imbens and Rubin (2015), Cambridge University Press. But we will cover a much broader range of topics.
Evaluation Six problem sets and a final project
Final Project The final project can be new methodological ideas, or a real application, or ideally both. It can also be, though less preferably, a report on a paper (or a set of papers) that deals with a topic that is related to the material covered in the class. Start early!
Statement (1) The material presented in the lecture notes reflect my own view and knowledge of the vast field of causal inference, which is by no means complete. All mistakes are mine. (2) If you use part of the material posted here for teaching or lecturing, please give proper acknowledgement. (3) The lecture notes will be periodically updated to reflect the trend of the field.
Acknowledgements I am grateful to Peng Ding, Alan Zaslavsky, Laine Thomas, Fabrizia Mealli, Alessandra Mattei, Constantine Frangakis, and Georgia Papadogeorgou for sharing valuable ideas and material. In particular, I thank Fan (Frank) Li at Yale Biostatistics (no joking, we have the same name :)) for writing parts of the slides on several lectures, including those on covariate adjustment, doublerobust estimators, sensitivity analysis, and sequential treatments.
Lecture Notes

Chapter 1. Introduction [slides]

Chapter 2. Randomized experiments
 Chapter 2.1: Fisher's, Neyman's mode of inference [slides]
 Chapter 2.2: Covariate adjustment in RCT [slides]

Chapter 3. Observational studies with ignorable assignments: singletime treatments
 Chapter 3.1. Outcome regression [slides]
 Chapter 3.2. Covariate balance, matching, stratification [slides]
 Chapter 3.3. Propensity score [slides]
 Chapter 3.4. Propensity score weighting: inverse probability weighting and overlap weighting [slides]
 Chapter 3.5. Augmented weighting and doublerobust estimators [slides]
 Chapter 3.6. Causal inference with multiple or continuous treatments [slides]
 Chapter 4. Sensitivity analysis [slides]

Chapter 5. Instrumental variable and principal stratification
 Chapter 5.1. Noncompliance, Instrumental variable (IV) approach [slides]
 Chapter 5.2. Posttreatment confounding: Principal Stratification [slides]
 Chapter 6. Regression discontinuity design (RDD) [slides]

Chapter 7. Panel data: Differenceindifferences (DID) and Synthetic control (SC) [slides]

Chapter 8. Sequentially ignorable assignments: timevarying treatments [slides]
 Chapter 9. Bayesian inference for causal effects [slides]

Chapter 10. Heterogenous treatment effects and machine learning [slides]