G19 RADIANCE Machine Learning & Causal EffectsInfo Location Attendee Categories Contact More Info Event Information
DescriptionThis intermediate course offers a comprehensive understanding of the links between machine learning and traditional methods in causal inference. This course was designed for participants that are familiar with causal inference and the fundamentals of Machine Learning.
Key Topics Covered : Potential Outcomes: Understand the concept of potential outcomes and how machine learning can be applied to estimate casual effcts in observational studies. Matching and Instrumental Variables: Explore advanced methods such as matching and instrumental variables to address confounding variables and strengthen casual inference models. Trees and Regularisations: Learn how decision trees and regularisation techniques can be intergrated into casual analysis, providing participants with tools to handle complex datasets and improve model performance. https://radiance.org.uk/machine-learning-and-causal-effects/
Event Location
Attendee CategoriesStandard Rate.
Additional ItemsContactFor queries in regards to this Course please contact the following :-
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 InformationRefund requests are accepted in full 14 days before the course. No refunds requested after that will be accepted.
Learning objectives There are two main learning objectives: · To develop an understanding of how Machine Learning is being applied for causal inference in real-world studies. · To be able to implement some of the available methods in R.
Course Structure There are three scheduled live online sessions, each consisting of a lecture and a hands-on computer practical session. During the practicals, participants will be provided with materials to implement the methods discussed in the respective lecture. Solutions will also be given to enable them to easily apply what they learn to their own work.
Timetable
Pre-requisite Participants should be familiar with causal inference, including key concepts such as confounding, treatment effects, and model evaluation is essential. In addition, attendees should have basic knowledge of Machine Learning to the level of the following suggested reading list:
· 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/
· | |||||||||||||||||||||||||||
London's global university
Managed by UCL Finance University College London - Gower Street - London - WC1E 6BT Email: [email protected]
© UCL 1999–2012, UCL is a Company Incorporated by Royal Charter (England/Wales), number: RC000631 VAT number: GB 524 3711 68
