Causal Inference in Epidemiology: A Practical Guide
Registration for the February 2027 course will open soon
Causal claims are central to scientific inquiry. We would like to invite registration for a two-day intensive training course covering the principles involved in making causal claims about health and disease. This course is designed to help researchers understand what causal claims mean, and how to judge their reliability.
We will cover a wide range of epidemiological study designs: including randomized trials, observational studies, and quasi-experimental approaches. We will discuss the strengths and weaknesses of each design, and think how evidence from each can be combined together to address a causal question. We will introduce various data analysis methods: including regression, g-estimation, inverse-probability weighting, doubly-robust estimation, and matching. However, the goal of the course is to give participants an appreciation of the assumptions on which causal claims rely and how to assess the validity of these assumptions, rather than detailed instructions how to perform any specific statistical technique.
Intended Audience
The course is written for participants working in epidemiology, public health, healthcare policy, evidence-based medicine, or health data science, who want to understand how causal claims are determined and evaluated. While examples will focus on epidemiology, the material will be broadly accessible to anyone working in the social sciences. The course will be particularly suitable for PhD students and early-career researchers both in academia, government, and industry.
Course Objectives
After the course, participants should be able to understand some of the core principles of causal inference, to apply these principles to the design and analysis of data, and to critically appraise approaches to causal inference in epidemiology.
Course Structure
For further information about the course, please contact: burgess-group-admin@mrc-bsu.cam.ac.uk