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PP5006 Machine Learning for Policy Analysis

Academic year

2026 to 2027 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Limited to students studying the Master of Public Policy.

Planned timetable

Mon 10am - 12 noon

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Prof D A Jaeger

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Prof David Jaeger

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Machine Learning for Policy Analysis equips students with practical skills to apply modern machine learning techniques to real-world public policy problems. The module covers supervised and unsupervised learning, text analysis, and causal machine learning. Emphasis is placed on interpretation, model validation, and the integration of machine learning with traditional policy evaluation methods. The module also addresses ethical, legal, and governance issues, including algorithmic bias, fairness, transparency, and accountability, ensuring students can critically assess the appropriate use of data-driven tools in decision-making.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS PP5001

Assessment pattern

Coursework= 100%

Re-assessment

Coursework= 100%

Learning and teaching methods and delivery

Weekly contact

2 hour lecture (x 10)

Intended learning outcomes

  • Apply core machine learning methods (e.g. regularised regression, tree-based models, clustering, and text analysis) to analyse policy-relevant datasets.
  • Prepare and manage complex data, including cleaning, feature engineering, and validation using appropriate training and testing frameworks.
  • Evaluate and compare predictive model performance using suitable metrics and interpret results in policy-relevant terms.
  • Integrate machine learning with causal policy analysis, including the use of methods to estimate heterogeneous treatment effects.
  • Assess ethical, legal, and governance implications of algorithmic decision-making, including issues of bias, fairness, transparency, and accountability.
  • ommunicate machine learning results effectively to non-technical policy audiences through clear written and visual outputs.