STA 640: Causal Inference

Fan Li

Department of Statistical Science, Duke University

  • Meeting times Spring 2023. Tuesday and Thursday 12:00-1:15pm, Social Science 136.
  • Instructor Fan Li, Statistical Science, Email: Office hours: Wednesday 8:30-9:30pm, Zoom; Friday 4:30-5:30pm in person (Old Chem 122)
  • 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. HWs are posted on Sakai.
  • 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, Joey Antonelli, Fabrizia Mealli, Alessandra Mattei, Constantine Frangakis, and Georgia Papadogeorgou for sharing valuable ideas and material. In particular, I thank Fan 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, double-robust estimators, sensitivity analysis, and sequential treatments.
  • Lecture Notes