In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
All announcements will be posted in Moodle.
|Week 2||Graphical models|
|Week 3||Causal graphical models|
|Week 4||Causal models and covariate adjustment|
|Week 5||Q&A session via Zoom|
|Week 6||Covariate adjustment|
|Week 7||Frontdoor criterion, instrumental variables and transportability|
|Week 8||Counterfactuals, potential outcomes and estimation|
|Week 9||Q&A session via Zoom|
|Week 10||Towards structure learning
|Week 11||Constraint-based causal structure learning
|Week 12||Score-based causal structure learning and restricted SEMs
|Week 13||LiNGAM and Invariant Causal Prediction (and face-to-face Q&A session)
|Week 14||Q&A session via Zoom|
There are two different exercise formats: Jupyter notebooks and exercise sheets. The Jupyter notebooks were intended to be worked on during in-class exercise sessions. However, as long as the course is taught online, you can work on both the Jupyter notebooks and the exercise sheets at home and ask questions during the question hours via Zoom.
Jupyter notebooksThe Jupyter notebooks can be found in this Renku project.
If you are a PhD student who needs ETH credit points, the submission of the
solutions to four exercise sheets is mandatory. If this applies to you, please email your solutions to
Students who need ECTS credit points have to take the exam.
The exercise sheets will be posted 2-3 weeks before the respective due date.
- Script by Jonas Peters and Nicolai Meinshausen.
- Peters, Janzing and Schölkopf (2017). Elements of Causal Inference. MIT Press.
- Freedman, Pisani and Purves (2007). Statistics. Fourth edition. Chapters 1-2.
- Maathuis, Drton, Lauritzen and Wainwright (2019). Handbook of Graphical Models. CRC Press.
- Shalizi. Advanced Data Analysis from an Elementary Point of View.
- Højsgaard, Edwards and Lauritzen (2012). Graphical Models with R.
- Pearl (2009). Causal inference in statistics: An overview.
- Spirtes, Glymour and Scheines (2000). Causation, Prediction and Search. MIT Press.
- Pearl (2009). Causality: Models, Reasoning and Inference. Wiley.
- Pearl, Glymour and Jewell (2016). Causal Inference in Statistics: A Primer. Wiley.
- Koller and Friedman (2009). Probabilistic Graphical Models. MIT Press.