Prof. Miguel Hernan (course co-ordinator)
Harvard T.H. Chan School of Public Health, Boston, USA
Prof. Marcel Zwahlen
Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland
Causal inference from observational data is a key task of epidemiology and of allied disciplines such as behavioural sciences and health services research. Commonly used statistical methods estimate association measures which cannot always be causally interpreted, even when all potential confounders are included in the analysis. In contrast, a causally explicit approach formally defines causal effects, identifies the conditions required to estimate causal effects without bias, and uses analytical methods that, under those conditions, provides estimates that can be endowed with a causal interpretation. This course presents such framework for causal inference from observational data and recent methodological developments, with a special emphasis on complex longitudinal data. The application of these methods will be illustrated using data from a synthetic HIV cohort study. The course is aimed at epidemiologists, statisticians, and other researchers who work with longitudinal observational data.
By the end of this short course participants will have
- An in-depth understanding of confounding and selection bias
- An understanding of the role and potential of different methodological approaches to overcome these problems, including inverse probability weighting, marginal structural models and nested structural models
- Practical data analysis experience using Stata®software.
Academic fee: CHF 900
Industry fee: CHF 2’000
SSPH+ fee: only applicable for students of the SSPH+ PhD Program in Public Health