401-4632-15L  Causality

SemesterSpring Semester 2019
LecturersC. Heinze-Deml
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-4632-15 GCausality2 hrs
Wed10-12HG E 3 »
C. Heinze-Deml

Catalogue data

AbstractIn statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
ObjectiveAfter this course, you should be able to
- understand the language and concepts of causal inference
- know the assumptions under which one can infer causal relations from observational and/or interventional data
- describe and apply different methods for causal structure learning
- given data and a causal structure, derive causal effects and predictions of interventional experiments
Prerequisites / NoticePrerequisites: basic knowledge of probability theory and regression

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersC. Heinze-Deml
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
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Offered in

ProgrammeSectionType
DAS in Data ScienceStatisticsWInformation
Data Science MasterCore ElectivesWInformation
Doctoral Department of MathematicsGraduate SchoolWInformation
Mathematics MasterSelection: Probability Theory, StatisticsWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation