Causal Inference Methods
Our main aim is to develop methods for understanding 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.
There are several new methods for causal inference, be it Bayesian networks, marginal structural models, counterfactual approaches etc, and our aim is to contribute to the development in this field.