Module Aims
This module aims to give students an appreciation of the principles of causal inference, understand why this matters, and consider ways of assessing causal inferences in practice.
Module Learning Outcomes
By the end of the module, students should be able to:
- Understand the principles of causal inference: why correlation is not causation, what causation is, and how one can demonstrate it in practice
- Apply the principles of causal inference to the design and analysis of data, as well as to critically appraise approaches for causal inference.
Pre-requisites
Principles of Biostatistics or Statistics for HDS (either core module).
Participants should read the first three chapters (36 pages) of Causal Inference by Hernán and Robins, available online at https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/.
Teaching Strategy
Lectures, small-group discussion, group project work, computer practicals.
Assessment
Part 1: Group presentation considering evidence on whether a modifiable exposure has a causal effect on a disease outcome, to be completed and presented in the last session of module (formative).
Part 2: Individual written project proposal outlining an innovative analysis to address a causal hypothesis, to be submitted a week after the end of the module (summative).
Module Length
4 days