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This repository consolidates my teaching material for "Causal Machine Learning".
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| 2026-07-31 | 265 |
| 2026-08-06 | 266 |
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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. **Last update: October 2025** ## 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](https://github.com/MCKnaus/causalML-teaching/issues) or [write me a mail](mailto:[email protected]) The slides include links to a variety of compiled html **R notebooks**. Their Rmd files are provided in [this repository](https://github.com/MCKnaus/causalML-teaching/tree/main/Notebooks) if you are interested in running and extending them yourself. A full list of available notebooks is provided on [my homepage](https://mcknaus.github.io/menu/teaching.html). ## Slides 0. [**Welcome**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML0_Welcome.pdf) 1. [**Stats/’metrics recap**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML1_metrics.pdf) 2. [**Supervised ML: predicting outcomes**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML2_SML.pdf) 3. [**Causal Inference basis**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML3_CI.pdf) 4. [**Estimating constant effects: Double Selection to Double ML**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML4_DS_PLR.pdf) 5. [**Average treatment effect estimation: AIPW-Double ML**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML5_AIPW.pdf) 6. [**Double ML - the general recipe**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML6_DML.pdf) 7. [**Heterogeneous effects**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML7_HTE.pdf) 8. [**Heterogeneous effects: validation and description**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML8_HTE2.pdf) 9. [**Policy learning**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML9_PL.pdf) 10. [**Multi-armed bandits**](https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML10_Bandits.pdf)  since Oct 2025 <!--- Started 16.10.2025 -->
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matched fp:427431b1c28f322d, llm:Repository description: "consolidates my teaching material for 'Causal Machine Learning'"
matched fp:427431b1c28f322d, llm:Repository description: "consolidates my teaching material for 'Causal Machine Learning'"