Machine Learning in Economics (ECON0126)

Key information

Faculty: Faculty of Social and Historical Sciences
Teaching department: Economics
Credit value: 15

Restrictions: Available to students on the following programmes only: - 果冻影院 MSc Economics, 果冻影院 MSc Data Science and Public Policy.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module will cover machine learning (ML) techniques 听and consider their applications to empirical economics. Coursework will include coding exercises, and so some prior programming experience is highly recommended. While students may use whichever language they like for assignments, solutions will be provided in R.

While ML is usually associated with prediction, ML methods can be adapted to problems of causal inference and the estimation of structural economic parameters. Techniques we will cover include penalized regression, tree-based methods, and deep learning. Some important applications include the estimation of heterogeneous treatment effects, estimation of average treatment effects under high-dimensional confounding, and instrumental variables estimation.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 100% Exam
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 57
Module leader: Dr Benjamin Deaner
Who to contact for more information: economics.dspp@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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