Causal Inference in Epidemiology: Concepts and Methods
An online short course
This course aims 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.
|Course date||7 - 9 June 2021|
|Course Organisers||Professor Jonathan Sterne, Professor Kate Tilling & Dr Abigail Fraser|
Please ensure you meet the following prerequisites before booking:
|Knowledge||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.|
|Software|| You must have Stata* (version 14, 15 or 16) installed in advance of the course.
*Internal University of Bristol participants will be provided with access to Stata version 16 on the first day of the course.
This 3-day course will run online, with approximately 18 hours of teaching. Sessions will be live, as will practical sessions.
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;
Online Course Bookings
Bookings are open for online courses running in 2021.
Find out more
We may need to make responsive changes to our courses at short notice in order to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).