Bianca De Stavola, Eduardo Fe and Andrea Aparicio Castro
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:
We will introduce the language of potential outcomes before describing the fundamental rules for drawing and interrogating causal diagrams.
This course is a pre-requisite for attending the advanced course called “Estimating Causal effects” which will run in November 2023.
To develop an understanding of the concept of potential outcomes and how they can be used to define targets of estimation and to learn to draw and interrogate causal diagrams.
There will be 2 on-line live lectures sessions and 1 on-line live discussion practical session.
Live session: Lecture 1
Live session: Lecture 2
Live session: Group work with DAGitty
Ahead of the course Participants should watch the RADIANCE appetiser called “Causal Questions”.
They might also wish to read chapter 1 of Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, which is available here:
This is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or masters students.