Causal Inference in Epidemiology: Concepts and Methods
Coronavirus (COVID-19) information
The Short Course Programme in Population Health Sciences has been temporarily suspended.
Due to demand, this 3 day course will run twice:
10 - 12 February 2020
29 June - 1 July 2020
3 days (approximately 18 hours teaching).
Registration will start at 9am on the first day, the course will finish by 5pm on the final day.
Bookings for 2020-21 courses will open later in the autumn.
Information on this page relates to the last run of the course and is for reference only.
Sign up to our mailing list to be notified when bookings reopen.
We may need to make responsive changes to our future programme to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).
Please be aware that all information about short courses planned for 2021 is provisional and subject to change.
To define causation in biomedical research, describe methods to make causal inferences in epidemiology and health services research, and demonstrate the practical application of these methods.
By the end of the course, students will:
- have a thorough understanding of the potential (counterfactual) outcomes approach to defining causal effects;
- implement Directed Acyclic Graphs (DAGs) to document assumptions and inform analysis plans;
- understand the key sources of bias in analyses of observational data, and how to investigate them using DAGs; and
- appreciate key methods which can be used to estimate causal effects, and understand the assumptions underlying them.
Who the course is intended for
This course is aimed at epidemiologists, statisticians and other quantitative researchers. Applicants must have knowledge and experience of a variety of linear and logistic regression models and their implementation in Stata, to beyond the level achieved in the 'Introduction to Linear and Logistic Regression Models' course. Familiarity with survival analysis is recommended. We recommend that you do not attend this course in the same year that you have attended 'Introduction to Linear and Logistic Regression Models'.
- Potential (counterfactual) outcomes;
- Causal diagrams (DAGs);
- Confounding and methods to control for confounding (stratification, regression, propensity scores and inverse probability weighting);
- Selection and information biases;
- Model selection;
- Instrumental variable estimation, including analysis of Mendelian randomization studies;
- Time-varying confounding, marginal structural models and other g-methods;
- Intention-to-treat and per-protocol effects in randomized trials;
- Emulating a randomized trial using observational data;
- Study designs for causal inference;
Please note: Practical sessions of this course will be held in a computer lab, so you will not need to bring a laptop.
More information on course fees, fee waivers and reduced prices.
Bristol Medical School
39 Whatley Road
We provide morning and afternoon refreshment breaks, including tea and coffee, biscuits and fresh fruit.
If you have specific dietary needs we ask that you let us know in advance.
Lunch is not included. There are a range of local cafes and supermarkets nearby for students to purchase lunch.
Information about accommodation in the area.
Related short courses
For further information please email firstname.lastname@example.org.