Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
10 Credits

Causal 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.

Causal Machine Learning
  • 10 Credits
  • 100 hours of study
  • 15 contact hours
  • 85 hours for private study
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Qualifications accredited by Lancaster University
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Buildable Qualifications
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Learn Around
Your Schedule
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World-Class
Faculty
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Fully Online

Structure

Software

Module Programme

Introduction

Session Content
  • Simpson’s Paradox
  • Directed Acyclic Graphs (DAG)
  • Potential Outcomes Causal Model
  • AB-testing and RCT Models

Standard approaches

Session Content
  • Regression and Double Orthogonalization
  • Propensity Scores
  • Doubly Robust Estimator

Machine Learning approaches

Session Content
  • High-dimension and Variable Selection
  • Post-Lasso and Post-Double-Lasso
  • Double/debiased Machine learning

Heterogeneous Treatment Effects

Session Content
  • Meta-Learners
  • Causal Forest
  • Generic Machine Learning

Methods

Session Content
  • Instrumental Variables
  • Difference-in-Differences
  • Synthetic Control

Session Content

Session Content

Session Content

Session Content

Session Content

Session Content

Session Content

Session Content

Prerequisites

English Language Requirements

Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.

 The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.

Academic Requirements

Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.

Learning Outcomes

Key Skills
  • Ability to comprehend concepts and principles of causal inference.
  • Proficiency in deploying advanced machine learning methods for causal analysis.
  • Skills to perform rigorous causal analysis using machine learning techniques.
  • Hands-on experience in deploying advanced ML methods for causal analysis.
Desired Skills
  • 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, andsocial sciences;
  • Communicate effectively about causal inference and its applications in machine learning.

Frequently Asked Questions

Are the courses within either programme conducted synchronously or asynchronously?

All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.

Is all examination undertaken online or in-person?

All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.

Do I need to buy any statistical/econometric software?

No, all necessary software is provided to students.

What do I do if I can't attend a course live?

All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.

A Collaboration Like No Other

Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.

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