Course duration
2 Months
Excluding orientation
Language
English
Access resources from start date
Effort
Effort
Self Study: 110 hours Tutorial: 20 hours Self-Test: 20 hours
Short Course Overview
DLMDSAM, DLMDSAS, DLMDSPWP
Distance Learning/Online Lecture
Duration: 2 Months (excluding orientation)
Statistical inference and causal analysis are crucial tools for analyzing and understanding data on a fundamental level. This course starts with an introduction to Bayesian inference and Bayesian networks which use probabilities to describe statistical problems and introduce probabilistic modelling which allows the specification of Bayesian statistical models in code. This course introduces the concepts of causality, how causality relates to correlation between variables, and discusses the fundamental building blocks of causal analysis. The effect of interventions (i.e.when the experimenter actively changes the setup from which the data are taken) are also discussed. This course then introduces the rules of do-calculus, which allow interventions to be described formally.Finally, the course discusses a wide range of typical fallacies which arise in the context of causal analysis.
Examinations
Admission Requirements BOLK: Yes
Course Evaluation: No
Type Exam: Workbook
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Short Course Overview
Course Information
Duration: 2 Months (excluding orientation)
Statistical inference and causal analysis are crucial tools for analyzing and understanding data on a fundamental level. This course starts with an introduction to Bayesian inference and Bayesian networks which use probabilities to describe statistical problems and introduce probabilistic modelling which allows the specification of Bayesian statistical models in code. This course introduces the concepts of causality, how causality relates to correlation between variables, and discusses the fundamental building blocks of causal analysis. The effect of interventions (i.e.when the experimenter actively changes the setup from which the data are taken) are also discussed. This course then introduces the rules of do-calculus, which allow interventions to be described formally.Finally, the course discusses a wide range of typical fallacies which arise in the context of causal analysis.
Examinations
Admission Requirements BOLK: Yes
Course Evaluation: No
Type Exam: Workbook
What will I learn?
Course Curriculum
Orientation module
Welcome to your Online Campus
Module 1
1. Statistical Inference
1.1 Bayesian inference.
1.2 Bayesian networks.
1.3 Probabilistic modelling.
Module 2
2. Introduction to Causality
2.1 Correlation vs causation.
2.2 Granger causality.
2.3 Directed Acyclic Graphs (DAG).
2.4 Elements of causal graphs: collider, chain, fork.
2.5 D – separation.
Module 3
3. Interventions
3.1 Seeing vs doing.
3.2 Conditional independence.
3.3 Confounders & counterfactuals.
3.4 Causal inference vs randomized controlled trials.
Module 4
4. Do-calculus
4.1 Front- & backdoor criterion.
4.2 Three rules of do-calculus.
Module 5
5. Fallacies
5.1 Mediation fallacy.
5.2 Collider bias.
5.3 Simpson’s & Berkson’s Paradox.
5.4 Imputing missing values: causal vs data-driven view.
Course prospectus
Suggested Short Courses
What our Students say about the course

Celine Rieder
My online studies are easy to combine with swimming practices because it is completely free of time and location restraints. This great flexibility lets me study easily between workouts.
Programme Instructor


Prof. Dr. Ulrich Kerzel
Inference and Causality.