Causal inference in practice
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