Teaching material for Causal ML
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.
Comments
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 iterested in running and extending them yourself. A full list of available notebooks is provided on my homepage.
Slides
- 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
- Predicting effects
- Heterogeneous effects with inference
- Policy learning