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Graphical models and causality
Last Updated: 2026-02-05 15:10:03
Abstract
This class is a weekly reading group discussing research papers on causality inference from observational or experimental data. The selected papers aim at understanding machine learning techniques to infer causality, including causal graphs derived from "graphical models”.
Objective
This class is a weekly reading group discussing research papers on causality inference from observational or experimental data. In a purely observational setting, quantities of interest (variables) can be recorded, but not acted upon. In an experimental setting, some controllable variables can be acted upon. The selected papers aim at understanding machine learning techniques to infer causality, including causal graphs derived from "graphical models”.
Resources
Lecture Notes
no
Literature
Several chapters of the book of Judea Peal "Causality" will be read. The other papers can be found on the web page of the class.
General Information
- Language
- English
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar | Graphical models and causality |
|
1 h weekly |