Eduardo Fe, Eleonora Iob and Andrea Aparicio Castro
This 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.
To 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 R
There will be 5 on-line live lectures sessions and 4 on-line live discussion/computer practical sessions.
Monday 4th December
Live session 1. Intro. to Machine Learning: Prediction.
Live session 2. Intro. to Machine Learning : Classification
Computer practical 1 (R)
Tuesday 5th December
Live session 3. Further topics in Machine Learning.
Computer practical 2 (R):
Live session 4.
Introduction to Machine Learning in Causal Inference .
Wednesday 6th December
Live session 5:
Machine Learning in Causal Inference under unconfounding.
Computer practical 3.
Computer practical 4.
It is advisable that, before attending this course, participants attend the following modules and short courses:
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.
Available for download at: https://www.statlearning.com/
Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
Available for download at: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Neal, B (2020) Introduction to Causal Inference from a Machine Learning Perspective (Lecture notes). https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf
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.