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TRAINING

DATA SCIENCE INTERMEDIATE

Introduction to Causal Diagrams
ON DEMAND

Bianca De Stavola and Andrea Aparicio Castro

This self-paced 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.

The course will give you an introduction to the concepts of potential outcomes and estimands before introducing the key elements (“building blocks”) for the construction of a causal diagram and the backdoor algorithm that we can employ to assess whether an observed association is affected by confounding.

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 are two pre-recorded lectures, and a practical task in DAGitty with solutions.

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:

https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2022/11/hernanrobins_WhatIf_13nov22.pdf

£75