Our main aim is to develop methods for causal inference; a hot subject in statistics and epidemiology.
About the group
Over the recent years statisticians have taken a much more proactive role in analyzing the causal conclusions that can be drawn from statistical data. In spite of the great challenges present in such an undertaking, there is no doubt that much of the medical knowledge concerning effects of treatments and disease prevention comes from statistical studies.
Various new methods for causal inference have been introduced over the last decades and the aim of this group is to contribute to the development in this field. In particular we work on causal inference in survival and time-to-event analysis; bayesian networks/causal directed acyclic graphs, mediation analysis, methods for time-dependent confounding, marginal structural models, etc.