F61 Causal Inference In Practice 2027Info Location Contact More Info Event Information
DescriptionAbstract Applied health and population health researchers are often asked to answer causal questions using empirical data: does an exposure affect later health, does a treatment work outside a trial, or does a policy or service change improve outcomes? These questions are difficult to answer using observational data alone, because associations may reflect confounding, selection bias, missing data, measurement error or inappropriate adjustment.
This three-day in-person course provides practical training in causal inference for researchers working with cohort studies, electronic health records, routinely collected data, trials with observational follow-up, administrative data and other population health datasets. The course introduces key causal inference frameworks and methods, including potential outcomes, directed acyclic graphs, target trial emulation, propensity score methods, regression adjustment, instrumental variable analysis and sensitivity analyses.
Each approach will be explained using applied examples, with emphasis on defining clear causal questions, making assumptions explicit, choosing appropriate analytical strategies, assessing robustness and interpreting results cautiously. Computer practicals will be provided in both R and Stata and will focus on implementing methods in realistic applied research settings.
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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 By the end of the course, participants will be able to:
Who should apply? This course is aimed at applied health researchers, epidemiologists, population health researchers, PhD students, postdoctoral researchers, researchers, analysts and clinicians in the public and private sectors who work with quantitative health data and want to strengthen their causal inference skills. Participants should be familiar with applied statistical analysis and regression modelling, ideally through an MSc in statistics, epidemiology, data science, population health or another quantitative subject. Pre-requisites Participants should have experience managing quantitative data and running regression models in either Stata or R, as well as knowledge of how to interpret model output. Participants should bring their own laptop with either Stata or 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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