Causal inference in observational epidemiology
Course 1
Date
15–17 January 2024
Faculty
Prof. Miguel Hernan
Harvard T.H. Chan School of Public Health, Boston, USA
Prof. Marcel Zwahlen
Institute of Social and Preventive Medicine, University of Bern, Switzerland
Venue
CH – 3823 Wengen | Hotel Jungfraublick
Course description
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.
Course objectives
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
- Done practical data analyses implementing these methods using Stata® or R software.
Course audience
The course is aimed at epidemiologists, statisticians, and other researchers who work with longitudinal observational data. Stata® or R (https://www.r-project.org/) will be used for the computer practical sessions, and so familiarity with the one of the two software applications is desirable, although code and solutions will be provided.
Course outline
The course runs over three days and consists of lectures, group work, and computer practical sessions.
We start early in the morning by reviewing the previous day. During the extended afternoon break, participants review course materials, catch up on email, or ski. We reconvene at 4:30 pm for the computer sessions.
Monday, 15 January 8:00 am – 12:00 pm | 4:30 pm – 6:30 pm
Tuesday, 16 January 8:00 am – 12:00 pm | 4:30 pm – 6:30 pm
Wednesday, 17 January 8:00 am – 12:00 pm | 1:00 pm – 3:00 pm
Credits
1.0 ECTS
Course materials
Bring a portable computer. A temporary course license for Stata® will be available to install before arrival. Instructions how to install Stata or R on your laptops will also be provided.
Onsite University of Bern IT staff provides support upon e-mail () request.
Course book
The course book—Causal Inference: What If (2020)—by M.A. Hernán and J.M. Robins is available now as a PDF.
Course fee
PhD Bern: CHF 600
PhD other: CHF 800
Academic: CHF 1000
Industry: CHF 2000
Registration
Accomodation
Book your accommodation separately. Please see recommendations for special prices.