Causality
Spring semester 2019
General information
Lecturer  Christina HeinzeDeml 

Assistant  Niklas Pfister 
Lectures  Wednesday 1012 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 3pm4pm in HG G 19.1.
 May 21st 2019: On May 22nd an inclass exercise session will take place from 11am12pm. There will be a normal lecture from 10:15am11am.
 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 inclass exercise session will take place from 11am12pm. There will be a normal lecture from 10:15am11am.
 March 27th 2019: We fixed a typo in the frontdoor criterion in the slides from week 5.
 March 6th 2019: On March 13th an inclass exercise session will take place from 11am12pm. There will be a normal lecture from 10:15am11am.
 February 8th 2019:
 A few times during the course, there will be inclass 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

Week 3  Causal graphical models

Week 4  Causal models and covariate adjustment 
Week 5  Covariate adjustment

Week 6  Instrumental variables and transportability

Week 7  Counterfactuals, potential outcomes and estimation

Week 8  Towards structure learning

Week 9  Constraintbased causal structure learning

Week 10  No class 
Week 11  No class 
Week 12  Causal structure learning

Week 13  Causal structure learning

Week 14  Invariant Causal Prediction

Week 15  No class 
Inclass exercises
A few times during the course, there will be inclass 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.Takehome 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 12.
 Maathuis, Drton, Lauritzen and Wainwright (2019). Handbook of Graphical Models. CRC Press.
 Shalizi. Advanced Data Analysis from an Elementary Point of View. Chapters 2025.
 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.