STA 790 (Special Topics): Bayesian Causal Inference
Fall 2022
Department of Statistical Science, Duke University
Meeting times and location Tuesday and Thursday 12noon  1:15pm, 8/30/20229/22/2022, Old Chem 025
Instructor Fan Li, Professor, Statistical Science, Email: fl35@duke.edu.
Office hours: Friday 2:303:30pm. Old Chem 122. Additional time is available by appointment.
Evaluation No formal homework, but I strongly encourage the students to implement some of the methods discussed in class
Statement (1) The material presented in the lecture notes reflect my own view and knowledge of the 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, Fabrizia Mealli, Constantine Frangakis, Joey Antonelli and Georgia Papadogeorgou for sharing valuable ideas and material.
Lecture Notes

Chapter 1. Introduction: Potential outcome framework, estimands, frequentist's identification [slides]

Chapter 2. General structure of Bayesian causal inference, different versions of estimands [slides]

Chapter 3. The role of propensity score in Bayesian causal inference [slides]
 Chapter 4. Heterogeneous treatment effects and model specification [slides]

Chapter 5. Sensitivity Analysis [slides]

Chapter 6. Complex assignment mechanisms I: principal stratification [slides]
References

Li F, Ding P, Mealli, F. (2022). Bayesian causal inference: a critical review. Philosophical Transactions of the Royal Society A. Forthcoming. [arxiv]

Ding P, Li F. (2018). Causal inference: a missing data perspective. Statistical Science. 33(2), 214237. [DOI  arXiv]

Linero A, Antonelli. (2022).
The how and why of Bayesian nonparametric causal inference.
Wiley Interdisciplinary Reviews: Computational Statistics
e1583. [arXiv]