## Teaching

### MasterMath Course Causality

Together with Patrick Forré, I developed and taught the MasterMath course on Causality in 2023. We will teach it again in 2024.

**Aim of the course**

Many questions in science are of a causal nature.
But how can we formalize the notion of causality?
How to reason about cause and effect mathematically?
How can we discover causal relations from data?
How to predict the consequences of actions?
How do causal predictions differ from ordinary predictions in statistics?
This course will address all these questions, making use of the mathematical frameworks of causal Bayesian networks and structural causal models.

**Topics**

- Causal modeling:
- definition of Markov kernels,
- conditional independences,
- causal Bayesian networks,
- structural causal models,
- marginalization,
- confounders,
- selection bias,
- feedback loops,
- causal graphs,
- interventions,
- Markov properties;

- Causal reasoning:
- intervention variables,
- do-calculus,
- counterfactuals,
- covariate adjustment,
- back-door criterion,
- identifiability;

- Causal discovery:
- randomized controlled trials,
- local causal discovery,
- Y-structures,
- FCI algorithm.

**Lecture notes**

The lecture notes we developed for this course are available for free.

**Prerequisites**

- bachelor level probability theory

(e.g., at the level of G. Grimmett and D. Welsh, 'Probability - An introduction', 2nd edition) - bachelor level measure theory

(e.g., at the level of R. Schilling, 'Measures, Integrals and Martingales' (2nd edition), Cambridge University Press, 2017) - bachelor level statistics

(e.g., at the level of F. Bijma, M. Jonker, A. van der Vaart, 'An introduction to Mathematical Statistics', Amsterdam University Press, 2017)

### SIKS Course on Causal Inference 2023

Thijs van Ommen and I organized the SIKS Course on Causal Inference for PhD students of the SIKS graduate school. Some of the course material is available here.

### Machine Learning Summer School 2019

Slides and exercises for my tutorial on Causality for the Machine Learning Summer School (MLSS) 2019 in Moscow, August 26-September 6, 2019.

### MSR AI Summer School 2018

Slides for my part of the lecture on Causality for the Microsoft Research AI Summer School 2018, July 2-6, 2018. The second part was given by Jonas Peters.

### ASCI APR Course 2016

Slides for my lecture in the ASCI APR Course on Causal Modelling, April 14, 2016.

### Introduction to Group Theory (for physicists)

- Lecture notes "applicatiecollege" (in dutch, gzipped PostScript, 9 augustus 2004).
- Vibrational Spectrum of Buckminsterfullerene (my Master's Thesis in Mathematics and visualizations of the eigenmodes).