Statistical Learning

1.General Information

• Instructor: 马骏
• Email: jun.ma@ruc.edu.cn
• Office: 北校区一号楼西配楼106
• Time: 周一18:00-20:25
• Venue: 明商206

2.Lecture Slides

1. syllabus
2. LT1 introduction
3. LT2 statistical learning
4. LT3 linear regression, updated: Oct 14
5. LT4 classification, updated: Oct 28
6. LT5 cv and bootstrap, updated: Oct 21
7. LT6 model selection, updated: Nov 04
8. LT7 nonlinear model, updated: Nov 16, 2022
9. LT8 tree, updated: Nov 23, 2022
10. LT9 deep learning
11. LT10 recap
12. final project, updated: Dec 26
13. LT11 adaptive LASSO
14. LT12 hd LASSO
15. LT13 double LASSO, updated: Dec 8
16. LT14 IV LASSO, updated: Dec 14, 2022
17. LT15 causal forests, updated: Dec 30
18. LT16 double ML
19. LyX模版，bib

3.Labs

1. R Markdown cheatsheets: 1, 2
2. R Markdown tutorial
3. R tutorial
4. lab1.rmd, lab1.pdf, Auto.csv, Auto.data
5. lab2.rmd, lab2.pdf
6. lab3.rmd, lab3.pdf
7. lab4.rmd, lab4.pdf
8. lab5.rmd, lab5.pdf
9. lab6.rmd, lab6.pdf
10. lab7.rmd, lab7.pdf
11. lab8.rmd, lab8.pdf
12. lab9.rmd, lab9.pdf
13. lab10.rmd, lab10.pdf
14. lab11.rmd, lab11.pdf
15. lab12.rmd, lab12.pdf

4. Reference

• Adaptive LASSO: Zou 2006
• Review papers: Belloni and Chernozhukov, Chernozhukov, Hansen and Spindler, Athey and Imbens
• High-dimensional econometrics in R
• hdm: high-dimensional metrics
• Double LASSO: Belloni, Chernozhukov and Hansen, 2014
• LASSO for IV: Belloni, Chen, Chernozhukov and Hansen, 2012; Chernozhukov, Hansen and Spindler, 2015
• Causal trees and forests: Athey and Imbens, 2016; Wager and Athey, 2018
• Causal forest application: Athey and Wager, 2019
• Inverse probability weighting estimation: Hirano, Imbens and Ridder, 2003
• Double LASSO for linear panel models: Belloni, Chernozhukov, Hansen and Kozbur, 2015; STATA code: https://statalasso.github.io/docs/pdslasso/pdslasso_panel/