Short courses


RADIANCE – Introduction to Causal Diagrams

  • 3 hours
  • Half a day (self-paced)


This introductory course is for anyone wishing to learn how to graphically draw our assumptions regarding how an exposure and an outcome may be related, either causally or via common associations with other variables. Learning about how to draw such assumptions is useful to guide: 

  1. the design of observational studies aiming to investigate the causal relationship between exposure and outcome and 
  2. the analysis of such studies. 

We will introduce the language of potential outcomes before describing the fundamental rules for drawing and interrogating causal diagrams. 

The course will consist of pre-recorded lectures, a computer practical and solutions.

This course is specifically designed to enhance knowledge, self-confidence and expertise among researchers who wish to develop their skills in analysing complex longitudinal biosocial data. It makes key concepts and approaches accessible to quantitative researchers from a wide range of disciplines and sectors, with particular emphasis on how these can be practically applied in their work. It is part of the RADIANCE programme, which has been developed by an expert team from UCL and the University of Manchester to offer comprehensive, state-of-the-art longitudinal data science training all in one place.

Who this course is for

This course is aimed at quantitative researchers working with biomedical and social data who wish to use causal diagrams in their research and would like a short introduction to this topic.

Course content

The course provides an introduction to the following topics:

  • the concepts of potential outcomes and estimands.
  • the key elements ('building blocks') for the construction of a causal diagram.
  • the backdoor algorithm that we can employ to assess whether an observed association is affected by confounding.

Teaching and structure

The course consists of two pre-recorded lectures, a computer practicals and solutions.

The software DAGitty will be used as part of the practicals, with full instructions and solutions provided.


A certificate of completion will be available to download once the required activities have been completed.

Learning outcomes

By the end of this course, you will be able to:

  • understand the concept of potential outcomes and how they can be used to define targets of estimation.
  • begin drawing and interrogating causal diagrams.

Cost and concessions

The standard fee for this course is £75.

A 10% discount is available for UCL students and staff – please email radiance@ucl.ac.uk for details.

Course team

Prof Bianca De Stavola

Prof Bianca De Stavola

Bianca is a Professor of Medical Statistics at UCL. Bianca’s areas of expertise include causal inference, analysis of longitudinal and survival data, growth modelling, and life course epidemiology.

Dr Eduardo Fe

Dr Eduardo Fe

Eduardo is a Senior Lecturer in Social Statistics at the University of Manchester. Eduardo’s areas of expertise include Causal Inference; Nonparametric Methods (Estimation and Inference of Conditional Moments; Bootstrapping); Panel Data; Labour and Experimental Economics. 

Dr Andrea Aparicio-Castro

Dr Andrea Aparicio-Castro

Andrea is an Associate Lecturer in Statistics at UCL. Andrea’s areas of expertise include multilevel and hierarchical modelling, data harmonisation and integration, estimation and forecasting, empirical Bayesian methods, data visualisation, and multivariate analysis.

Learner reviews

100% of survey respondents felt that the module was stimulating and helped make the subject interesting. Respondents commented that the course provided "a fantastic overview" of the topic, and that the practical exercise was "engaging and thought-provoking".

Course information last modified: 25 Jan 2024, 15:38