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.
- March 17th 2020: From now on, all announcements will be posted in Moodle.
- March 11th 2020: The lecture recordings can be found in Moodle.
- March 9th 2020: Starting this week, the lectures will be recorded. Since I cannot be in Zurich this Wednesday (March 11th), this week's lecture will be pre-recorded and will be available *only* electronically. I will send you the information where to find the video on Wednesday.
- February 10th 2020:
- A few times during the course, there will be in-class exercise sessions instead of a normal lecture. During those sessions, we will work with jupyter notebooks and R. Please make sure to install jupyter and the required R packages beforehand. Installation instructions are given here.
- We will be using the ETH EduApp during the lectures for clicker questions. Please install it on your phone or tablet (iOS, Android). If this is not possible, you can also access the EduApp via the Web App if you bring a laptop to class.
|Week 2||Graphical models
|Week 3||Causal graphical models|
|Week 4||Causal models and covariate adjustment|
|Week 5||Exercise session: Please see Moodle for further information.|
|Week 6||Covariate adjustment|
|Week 7||Instrumental variables and transportability|
|Week 8||Counterfactuals, potential outcomes and estimation|
|Week 9||No class (Easter break)|
|Week 10||Exercise session: Please see Moodle for further information.|
|Week 11||Towards structure learning|
|Week 12||Constraint-based causal structure learning|
|Week 13||Score-based causal structure learning and restricted SEMs|
|Week 14||LiNGAM and Invariant Causal Prediction|
|Week 15||Exercise session: Please see Moodle for further information.|
In-class exercisesA few times during the course, there will be in-class exercise sessions instead of a normal lecture. During those sessions, we will work with jupyter notebooks and R. These are provided in this Github repository.
If you are a PhD student who needs ETH credit points, the submission of the solutions is mandatory. If this applies to you, please email your solutions to Loris Michel or place them in the corresponding tray in HG J 68. Students who need ECTS credit points have to take the exam.
- 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. Chapters 20-25.
- 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.