MATH 598: Topics in Statistics
Winter 2021
An Introduction to Causal Inference Methods

Lecture Slides

  1. Slides Part 1
  2. Slides Part 2
  3. Slides Part 3
  4. Slides Part 4
  1. Tsiatis commentary
  2. Henmi & Eguchi commentary

Projects

  1. Project 1 Solutions
  2. Project 2 Solutions
  3. knitr
  4. Project 3 Solutions
  5. knitr
  6. Project 4 Solutions
  7. Project 5 Solutions
  8. Project 6 Solutions

 

Exercises

 

knitr style file 
1. knit-01: Simpson's Paradox pdf Rnw
2. knit-02: Conditioning on a collider pdf Rnw
3. knit-03: d-separation pdf Rnw
4. knit-04: Experimental studies pdf Rnw
5. knit-05: Outcome Regression pdf Rnw
6. knit-06: Colliders and backdoor paths pdf Rnw
7. knit-07: Causal Adjustment pdf Rnw
8. knit-08: IPW estimation pdf Rnw
9. knit-09: AIPW estimation pdf Rnw
10. knit-10: Causal Adjustment pdf Rnw
11. knit-11: Adjustment with estimation pdf Rnw
knit-11: Second version (different p.s.) pdf Rnw
12. knit-12: Variance Results pdf Rnw
knit-12: Second version (different p.s.) pdf Rnw
13. knit-13: Augmented outcome regression pdf Rnw
14. knit-14: Seimparametric calculations for the Linear Model pdf Rnw
15. knit-15: Log-Linear Models pdf Rnw
16. knit-16: ATT estimation pdf Rnw
17. knit-17: MSM pdf Rnw
18. knit-18: DTRs pdf Rnw
. knit: NHANES example pdf Rnw
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Contact Details:
Professor David A. Stephens
Room 1225, Burnside Hall
Department of Mathematics and Statistics
McGill University



E-mail : David Stephens