Causal Inference in Epidemiology: Concepts and Methods

This 5-day course will run online and will consist of a mixture of lectures, small group work and computing practicals. Computer practicals take place in virtual breakout rooms with the help of tutors, using participants’ own computers or our Posit Cloud environment. All sessions will be live.

By the end of the course participants should be able to:

1. describe the potential (counterfactual) outcomes approach to defining causal effects;
2. use Directed Acyclic Graphs (DAGs) to document assumptions and inform analysis plans;
3. recognise the key sources of bias in causal analyses of observational data, and how to investigate them using DAGs;
4. define a research question comparing health interventions using a hypothetical ‘target trial’;
5. apply key methods to estimate causal effects by emulating a target trial, and recognise the situations in which they are appropriate and the assumptions underlying them;
6. recognise the challenges of making causal inferences about effects of exposures.

This advanced course is aimed at epidemiologists, statisticians and other quantitative researchers. Applicants must have knowledge and experience of a variety of regression models, including Cox models for time-to-event data, and their implementation in Stata or R. Such knowledge must be to beyond the level achieved in the Introduction to Linear and Logistic Regression Models course. We recommend that you do not attend this course in the same year that you have attended Introduction to Linear and Logistic Regression Models.

The course will cover:

1. potential (counterfactual) outcomes;
2. causal diagrams (DAGs);
3. confounding and methods to control for confounding (stratification, regression, propensity scores and inverse probability weighting);
4. selection and information biases;
5. inverse probability weighting to deal with informative censoring
6. target trials to define a causal question about health interventions
7. instrumental variable estimation;
8. intention-to-treat and per-protocol effects in randomized trials and observational studies;
9. time-varying confounding, marginal structural models and other g-methods;
10. sequential approaches to emulating a target trial using observational data;
11. avoiding bias caused by immortal time: the clone-censor-IP weight approach;
12. model selection for causal inference studies
13. study designs for causal inference; and
14. reporting and triangulating causal inference studies

The teaching faculty for this course include:

Professor Jonathan Sterne
Professor Kate Tilling
Dr Kimberley Burrows
Dr Chin Yang Shapland
Dr Kate Birnie
Dr Paul Madley-Dowd
Dr Tom Palmer
Dr Venexia Walker

To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:

Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.

Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms and conditions, which can be read in full here.

For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.

Participants are granted access to our virtual learning platform (Blackboard Ultra) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.

To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.

All course participants retain access to the online learning materials and recordings for 5 months after the course. 

University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials and Recordings' version of this course: Further information and bookings.

93% of attendees recommend this course*.
*Attendee feedback from 2025.

Here is a sample of feedback from the last run of the course:

“Fantastic course. Really intense but in a good way - well worth the time and money." - Course feedback, July 2026.

“Good mix of lectures and practicals. Practicals helped consolidate concepts. Enjoyed the mentis. Great that all sessions were live. Course leads very helpful at answering questions and the chat going on throughout." - Course feedback, July 2026.

“I liked how the learning built up over the course, where you were able to apply the concepts you learnt earlier in the course as it went on. I also liked the opportunity to consolidate key learning through the practicals and the menti at the end of the day." - Course feedback, July 2026.

“It was a great summary of many different concepts related to causal inference. It was well paced and very well delivered by the faculty. Highly recommend!" - Course feedback, July 2026.

“Lecturers were all excellent with lots of time/patience for additional questions. Good balance between lectures and practicals." - Course feedback, July 2026.

“The broad coverage of this course has provided me with awareness and inspiration regarding what needs to be fixed and explored before running an analysis involving causal inference and how to achieve this. I plan to return to solutions to exercises to review my learning." - Course feedback, July 2026.

“The course is very well structured, I particularly liked the order of the lectures, the variety of the topics and the number of teachers providing the lectures." - Course feedback, July 2026.

“The course provided invaluable insights into nuances of causal inference that would typically be absent from standard methods courses focused on a particular type of statistical analysis. I appreciated the commitment to keeping us informed with the state of the art in causal inference, with the provision of handy links and relevant publications." - Course feedback, July 2026.

“The course tutors are extremely knowledgable [sic] and engaging. This is a challenging course but worth the time and effort to learn some fundamental and cutting edge concepts delivered by experts in the field." - Course feedback, July 2026.

“The fact that the sessions were delivered by amazingly knowledgeable tutors who were not only able to deliver very complicated topics effectively but also in parallel answer questions and clarify concepts on the spot. The practical materials provided were very effective too and even in both STATA and R!" - Course feedback, July 2026.

“The practical sections were particularly valuable, with extensive hands-on content that reinforced the concepts taught." - Course feedback, July 2026.

“The team are excellent. Extremely knowledgeable, responsive to questions, and skilled facilitators. The materials are comprehensive." - Course feedback, July 2026.