Machine Learning for Prediction and Causal Inference

RADIANCE courses are targeted to the broad community of UK data scientists working in public health. They include epidemiologists, clinicians, data engineers/informaticians, statisticians, as well as quantitative researchers from other disciplines (e.g. psychology, social sciences, health economics).They may be from academia, charities, government departments and non-profitable organisations.

RADIANCE

This course will introduce a variety of machine learning (ML) methods to analyse numerical and categorical outcomes.

Lecturers Eduardo Fé, Eleonora Iob and Andrea Aparicio CastroCourse descriptionThis course will introduce a variety of machine learning (ML) methods to analyse numerical and categorical outcomes, and it will discuss how they can be applied in both prediction and causal inference settings. This is an introductory course, and therefore ideas will be explained at a beginner level, with a particular focus on practical applications of ML in real-world studies. Tutorials will include readily available methods and solutions that participants will be able to apply in their own work.Learning objectivesTo develop an understanding of how Machine Learning is being applied for prediction and causal inference in real-world studies. To be able to implement some of the available methods in RCourse StructureThere will be 5 on-line live lectures sessions and 4 on-line live discussion/computer practical sessions.

Pre-requisite
It is advisable that, before attending this course, participants attend the following modules and short courses:
•	“Addressing Causal Questions using real work data: an Introduction” (Module)
•	“Regression Models” (Module)
•	 “Introduction to Causal Diagrams” (Short course) 
Students can also access the free online books:
James, G., D. Witten, T. Hastie, R. Tibshirani (2021) Introduction to Statistical Learning with Applications in R, 2nd Edition. Springer Texts in Statistics (<a href="https://www.statlearning.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">available here</a>)Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman &amp; Hall/CRC.
 (<a href="https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">available here</a>)Neal, B (2020) Introduction to Causal Inference from a Machine Learning Perspective (Lecture notes). (<a href="https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">available here</a>)
More information
This is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or Masters students
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Machine Learning for Prediction and Causal Inference

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Machine Learning for Prediction and Causal Inference

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