
After following this course, the student is able to:
 explain the strength/weakness behind different approaches for causality
 propose causal models for data
 explain and judge the assumptions underlying different approaches
 do parameter estimation in causal models
 explain the link between causal models and machine learning


Does smoking cause lung cancer? This has been a highly disputed question in the 50ies when the number of lung cancer deaths surged. In those years the number of smokers increased as well. This suggests a relationship, but this could have been also a pure coincidence. In fact, there were many other possible explanations ranging from car emissions to a genetic disposition that would cause people to be more attracted to smoking and at the same time cause lung cancer. If the latter would be true, prohibiting smoking would not influence the number of lung cancer cases.
So how can we formally establish a causal relationship? The course will cover several concepts of causal relationships and how to infer those from observational data. The theory is applicable to a wide range of applied sciences and is particularly relevant for medical data. In gene studies, for instance, we measure large numbers of variables and typically observe correlations among many of these variables. To find the genetic mechanism behind a disease, this is, however, not enough and we need to uncover the causal pathway.
The lectures cover
 Introduction and examples
 Estimation of average treatment effect
 Undirected graphical models
 Directed acyclic graphs (DAGs)
 Causal graphs
 Structural equation models
 Linear structural equation models
 Instrumental variables
 Structure learning, causal discovery




 VoorkennisStudents should have solid knowledge of statistics, including knowledge of probability, expectation, mean, variance, discrete and continuous distributions, confidence intervals, simple linear regression. For instance: ‐ Probability Theory (202001344) and Mathematical Statistics (202001348) or ‐ Probability Theory (202001233) and Statistical Techniques (202001033) 
Master Applied Mathematics 
Master Electrical Engineering 
Master Technical Medicine 
Master Biomedical Engineering 
Master Business Information Technology 
  Verplicht materiaalBookJonas Peters, Dominik Janzing,
Bernhard Schölkopf: Elements of Causal
Inference, MIT Press, 2017. The book is open access and can be downloaded
via https://mitpress.mit.edu/books/elementscausal‐inference . ISBN 9780262037310. 

 Aanbevolen materiaalBookSteffen Lauritzen: Graphical Models. Oxford University Press, New York NY, 1996. ISBN 0198522193. 

 WerkvormenToetsenExam


 