MCKnaus / causalML-teaching

This repository consolidates my teaching material for "Causal Machine Learning".
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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 interested in running and extending them yourself. A full list of available notebooks is provided on my homepage.

Slides

  1. Welcome
  2. Stats/’metrics recap
  3. Supervised ML: predicting outcomes
  4. Causal Inference basis
  5. Estimating constant effects: Double Selection to Double ML
  6. Average treatment effect estimation: AIPW-Double ML
  7. Double ML - the general recipe
  8. Heterogeneous effects
  9. Heterogeneous effects: validation and description
  10. Policy learning
  11. Multi-armed bandits