TRAINING

COURSES

Machine Learning for Prediction and Causal Inference

4 - 6 December 2023

Lecturers

Eduardo Fe, Eleonora Iob and Andrea Aparicio Castro

Course description

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.

Learning objectives

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

Course Structure

There will be 5 on-line live lectures sessions and 4 on-line live discussion/computer practical sessions.

Timetable

Date	Time
Monday 4^th December	9:30-10:30am	Live session 1. Intro. to Machine Learning: Prediction.
	10:30:10:45	BREAK
	10:45-11:30am	Live session 2. Intro. to Machine Learning : Classification
	11:30-11:45	BREAK
	11:45-13:00	Computer practical 1 (R)
Tuesday 5^th December	9:30-10:30am	Live session 3. Further topics in Machine Learning.
	10:30:10:45	BREAK
	10:45-11:30am	Computer practical 2 (R):
	11:30-11:45	BREAK
	11:45-13:00	Live session 4. Introduction to Machine Learning in Causal Inference .
Wednesday 6^thDecember	9:30-10:30am	Live session 5: Machine Learning in Causal Inference under unconfounding.
	10:30:10:45	BREAK
	10:45-11:30am	Computer practical 3.
	11:30-11:45	BREAK
	11:45-13:00	Computer practical 4.

Pre-requisite

It is advisable that, before attending this course, participants attend the following modules and short courses:

“Addressing Causal Questions using real world 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, 2^nd 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

Fee

Free.

Registration

https://www.eventbrite.co.uk/e/machine-learning-in-causal-effects-tickets-492278005767

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.