F61 Causal Inference In Practice 2025Info Location Contact More Info Event Information
DescriptionAbstract A wide range of fields, from clinical medicine to social science, seek to use empirical data to learn how different factors affect the world. Making credible causal claims can be very difficult, particularly using observational rather than experimental data. Causal inference provides a framework to clarify and assess the assumptions on which causal interpretation depends and develop statistical tools which can be implemented in practice.
This course covers the fundamental developments in causal inference methods and gives practical explanations about how to apply these methods to real research questions. The course will cover: potential outcomes, target trials, propensity score, and instrumental variable analysis. Each approach will be explained within the causal inference framework, along with the recommended sensitivity analyses, validation, and specification tests to assess the plausibility of the analysis, where applicable.
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ContactFor all queries in regards to this Course please contact the following :- Neil Davies
PLEASE ONLY CONTACT THE ONLINE STORE DIRECTLY IF YOU ARE EXPERIENCING PROBLEMS WITH YOUR DEBIT/CREDIT CARD PAYMENT, FOR ALL OTHER QUERIES RELATING TO THIS COURSE, INCLUDING CANCELLATIONS THESE SHOULD BE DIRECTED TO THE CONTACT DETAILS ABOVE.
More InformationIntended learning objectives: Understand: 1. the potential outcomes framework and how to apply it; 2. how to use directed acyclic graphs; 3. estimate causal effects using observational data and the assumptions required; and 4. interpret causal analyses.
Who should apply? This course is aimed at people conducting applied epidemiological, medical and other quantitative research, from PhD students to experienced researchers interested in learning more about causal inference methods. Participants should have familiarity with applied statistical analysis and a MSc in statistics, data science, or other quantitative subject. Pre-requisites: experience managing data and running regression models in either Stata or R (as well as knowledge on how to interpret model output). Participants should have their own laptop with either Stata and R already installed.
Tutors Karla Diaz-Ordaz (Department of Statistical Science), Neil Davies (Division of Psychiatry and Department of Statistical Science), and Peter Martin (Institute of Epidemiology and Health Care) | ||||||||||||||||||||||||
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