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
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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
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