Causality
Spring semester 2019
General information
| Lecturer | Christina Heinze-Deml |
|---|---|
| Assistant | Niklas Pfister |
| Lectures | Wednesday 10-12 HG E 3 >> |
| Course catalogue data | >> |
Course content
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.
Announcements
- July 2nd 2019: On July 30th and 31st, question hours will take place from 3pm-4pm in HG G 19.1.
- May 21st 2019: On May 22nd an in-class exercise session will take place from 11am-12pm. There will be a normal lecture from 10:15am-11am.
- April 12th 2019: No class on May 29th.
- April 11th 2019: We have expanded the solutions to exercise 1 in series 3.
- April 3rd 2019: On April 10th an in-class exercise session will take place from 11am-12pm. There will be a normal lecture from 10:15am-11am.
- March 27th 2019: We fixed a typo in the frontdoor criterion in the slides from week 5.
- March 6th 2019: On March 13th an in-class exercise session will take place from 11am-12pm. There will be a normal lecture from 10:15am-11am.
- February 8th 2019:
- 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.
Course materials
| Week | Topic |
|---|---|
| Week 1 | Introduction
|
| Week 2 | Graphical models
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| Week 3 | Causal graphical models
|
| Week 4 | Causal models and covariate adjustment |
| Week 5 | Covariate adjustment
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| Week 6 | Instrumental variables and transportability
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| Week 7 | Counterfactuals, potential outcomes and estimation
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| Week 8 | Towards structure learning
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| Week 9 | Constraint-based causal structure learning
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| Week 10 | No class |
| Week 11 | No class |
| Week 12 | Causal structure learning
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| Week 13 | Causal structure learning
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| Week 14 | Invariant Causal Prediction
|
| Week 15 | No class |
In-class exercises
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. These are provided in this Github repository.Take-home exercises
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 Niklas Pfister or place them in the corresponding tray in HG J 68. Students who need ECTS credit points have to take the exam.
| Exercises | Solutions | Due date |
|---|---|---|
| Series 1 | Solutions 1 | March 13th 2019 |
| Series 2 | Solutions 2 | March 20th 2019 |
| Series 3 | Solutions 3 (updated 04/11) | April 3rd 2019 |
| Series 4 | Solutions 4 | April 17th 2019 |
| Series 5 | Solutions 5 | May 15th 2019 |
Literature
- 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.