This repository consolidates the teaching material of several "Causal Machine Learning" courses I taught on the master and PhD level with a focus on impact/policy/program evaluation.
!!! Workshop for Ukraine April 11th: Do you like the material and my style of teaching? Please join my short course "Introduction to Causal Machine Learning estiamtors in R" or fund students who want to join here !!!
Like the whole literature the content is a moving target. Please let me know if you spot any errors, disagreements, but also if you found the material useful. To this end, open an issue or write me a mail
The slides include links to a variety of compiled html R notebooks. Their Rmd files are provided in this repository if you are interested in running and extending them yourself. A full list of available notebooks is provided on my homepage.
- Welcome
- Stats/’metrics recap
- Supervised ML: predicting outcomes
- Causal Inference basis
- Estimating constant effects: Double Selection to Double ML
- Average treatment effect estimation: AIPW-Double ML
- Double ML - the general recipe
- Heterogeneous effects
- Heterogeneous effects: validation and description
- Policy learning
- Multi-armed bandits