The course Causality is a "Spezialgebiete" lecture (4.5 ETCS) that can be elected for students in the following Master's programs: M. Sc. Statistics, M. Sc. Data Science, M. Sc. Econometrics.
During the course, the following topics will be covered:
- Graphical Models (DAGs, Causal Bayesian Networks, Markov properties)
- Causal Inference (Do-Calculus, Backdoor- and Frontdoor Criterion, Instruments, Counterfactuals)
- Causal Discovery (Fundamental Assumptions, Constraint-Based Algorithms, Score-Based Algorithms)
- Causal Machine Learning
Importierter Kurs aus dem LSF
During the course, the following topics will be covered:
- Graphical Models (DAGs, Causal Bayesian Networks, Markov properties)
- Causal Inference (Do-Calculus, Backdoor- and Frontdoor Criterion, Instruments, Counterfactuals)
- Causal Discovery (Fundamental Assumptions, Constraint-Based Algorithms, Score-Based Algorithms)
- Causal Machine Learning
Importierter Kurs aus dem LSF
- Lehrende:r: Alexander Marx