holyfantasticyc/causalml-teaching

This repository consolidates my teaching material for "Causal Machine Learning".

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

0. Welcome
1. Stats/’metrics recap
2. Supervised ML: predicting outcomes
3. Causal Inference basis
4. Estimating constant effects: Double Selection to Double ML
5. Average treatment effect estimation: AIPW-Double ML
6. Double ML - the general recipe
7. Predicting effects
8. Heterogeneous effects with inference
9. Policy learning