Lecturer: Jan Beyersmann
|English, unless all students have sufficient knowledge of German|
|Wednesday 18.07.2018, 12:00 a.m. (He 22 E04)|
Elementary Probability Calculus, Stochastic I, Measure an Integration Theory, Basic Programming Skills.
The level of the course is roughly that of a first year's master course in Mathematical Biometry. Basic knowledge of R is helpful, but not mandatory.
There are two reasons for a statistical analysis. One is prediction of future data based on what one has learned from past data and accounting for uncertainty. Prediction need not be concerned with understanding cause-effect relationships, but understanding causality is central to our understanding of data and how we use that knowledge. For instance, standard statistical techniques allow to predict the survival probability of a current smoker, typically predicting earlier death compared to non-smokers. But there is no standard statistical technique that analyses the causal effect of smoking on mortality. The difficulty is that smoking is not assigned in a randomized experiment, and there are more differences between smokers and non-smokers than just smoking status. In fact, defining a causal effect is not even part of the usual statistical and mathematical formalism. In the last 30 years or so, there has been a statistical revolution of developing causal inference, motivated by practical needs as in the search for effective HIV treatments. The aim of this lecture is to introduce students to this groundbreaking new field
Will be available on moodle. You may turn in solutions in pairs (two students, one solution; but not more than two).
Pearl: Causality, Cambridge University Press, 2009
Aalen, Borgan, Gjessing: Survival and Event History Analysis, Springer 2008