top of page
Laptop Office Worker

Causal Machine Learning

Please note to take this course you must first have completed Foundations of Data Science or Foundations of Econometrics & Machine Learning

This course is an introduction to the principles and practices of causal

machine learning. It covers the basics of causal inference, including the identification and estimation of causal effects, as well as the applications of causal machine learning in areas such as health, finance, and social sciences. The course also includes an introduction to common methods and algorithms for causal machine learning, such as instrumental variables, synthetic controls, AB-testing and propensity scores.

This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

By the end of this course, participants should have knowledge and understanding of: 

The basis and motivation for Bayesian Causal Inference Understanding: Ability to comprehend concepts and principles of causal inference.

 

Advanced ML Methods for Causal Analysis: Proficiency in deploying advanced machine learning methods for causal analysis.

Causal Analysis Skills: Skills to perform rigorous causal analysis using machine learning techniques.

 

Application of Advanced ML: Application of advanced machine learning methods specifically for causal inference.

Practical Deployment: Hands-on experience in deploying advanced ML methods for causal analysis.

Interpretation of Causal Relationships: Ability to interpret and derive causal relationships from data.

Critical Thinking in Causal Inference: Developing critical thinking skills for causal inference scenarios.

Problem-Solving in Causal Analysis: Addressing real-world problems through causal analysis using advanced ML.

Desired Skills

By the end of this course, students should be able to: 

Understand the basics of causal inference and its role in machine learning.

Identify and estimate causal effects using common methods and algorithms.

Apply causal machine learning to real-world problems in areas such as health, finance, and social sciences.

Communicate effectively about causal inference and its applications in machine learning.

Understand that instrumental variables is not the only tool to deal with endogeneity issues.

Understand the principle of causal inference.

Identify and estimate causal effects using common ML methods and algorithms.

Apply causal machine learning to real-world problems in areas such as health, finance, and social sciences.

Structure

 

Causal Machine Learning is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.

 

The total number of contact hours is 15 hours. This leaves 85 hours for private study.

Lectures

This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day. 

During their private study, students should read the course literature, work on practical exercises, and write a project report for the examination.

The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.

bottom of page