Causality
Spring semester 2017
General information
Lecturer  Marloes Maathuis 

Assistant  Christina HeinzeDeml 
Lectures  Thu 810 HG D 7.1 >> 
Course catalogue data  >> 
Course content
In statistics, we often consider prediction or classification problems. Many interesting questions in the sciences, however, do not fall within this framework. In this course we will consider questions of a causal nature, that is, questions about the causal mechanism behind a system or about predicting a system's behavior under manipulations. We will study concepts, theory and applications of modern causal inference methods.
Announcements

August 15th 2017:
The question hour on August 21st (9:30  11:00 am) will take place in HG G 19.1. 
July 16th 2017:
We will hold another question hour on August 21st, please find the details here. 
May 23rd 2017:
We will hold a question hour on August 14th, please find the details here. 
May 16th 2017:
We fixed a typo in series 3 (exercise 3). 
May 15th 2017:
Please note: The pcalg package depends on the packages ‘graph’, ‘RBGL’, and ‘Rgraphviz’, which need to be installed from Bioconductor. 
May 10th 2017:
 No class on May 18th and May 25th.
 The last series will be posted on May 12th and will be due on May 19th.

May 9th 2017:
The oral exams will take place in the period August 21  September 1. 
May 4th 2017:
Updated slides and R code for week 10; added slides for week 8 and R code for week 11. 
March 30th 2017:
No class on April 6th and April 20th. 
March 23rd 2017:
Updated slides on covariate adjustment (week 4). 
March 16th 2017:
Homework 1 is due on Monday, March 20th 2017. Please email your solutions to Christina HeinzeDeml or place them in the corresponding tray in HG J 68.
The submission of solutions is only mandatory for PhD students who need ETH credit points. Students who need ECTS credit points have to take the exam. 
March 10th 2017:
Updated slides on DAGs (week 2). 
February 8th 2017:
Beginning of lecture: Thursday, 23/02/2017.
Course materials
Week  Topic 

Week 1  Introduction

Week 2  Graphical models

Week 3  Causal DAG models 
Weeks 4 and 5  Causal DAG models and covariate adjustment 
Week 6  Counterfactuals and instrumental variables

Week 7  No class 
Week 8  Markov assumptions, faithfulness assumption, minimal Imap, perfect map, Markov equivalence, CPDAG 
Week 9  No class 
Week 10  Constraintbased structure learning

Week 11  Scorebased structure learning 
Week 12  LiNGAM 
Exercises
Please email your solutions to Christina HeinzeDeml or place them in the corresponding tray in HG J 68. The submission of solutions is only mandatory for PhD students who need ETH credit points. Students who need ECTS credit points have to take the exam.
Exercises  Solutions  Due date 

Series 1  Solutions 1  March 20th 2017 
Series 2  Solutions 2  April 13th 2017 
Series 3,
data set: structurelearning.rda.
Please note: The pcalg package depends on the packages ‘graph’, ‘RBGL’, and ‘Rgraphviz’, which need to be installed from Bioconductor. 
Solutions 3  May 19th 2017 
Literature
 Freedman, Pisani and Purves (2007). Statistics. Fourth edition. Chapters 12.
 Shalizi. Advanced Data Analysis from an Elementary Point of View. Draft available here. Chapters 2025.
 Højsgaard, Edwards and Lauritzen (2012). Graphical Models with R.
 Pearl (2009). Causal inference in statistics: An overview. Available here.
 Spirtes, Glymour and Scheines (2000). Causation, Prediction and Search. MIT Press. Available here.
 Pearl (2009). Causality: Models, Reasoning and Inference. Wiley.
 Pearl, Glymour and Jewell (2016). Causal Inference in Statistics: A Primer. Wiley.
 Peters, Janzing and Schölkopf (2017). Elements of Causality. MIT Press. Draft available here.
 Koller and Friedman (2009). Probabilistic Graphical Models. MIT Press. Available here.