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PP5001 Causal Inference for Policy Analysis

Academic year

2026 to 2027 Semester 1

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

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

This module will provide foundational training in techniques for policy analysis on experimental and non-experimental data. The goal for students is to emerge from the module with a 鈥渢oolkit鈥� of methods for estimating plausibly causal effects of policies. These causal estimates are essential for evidence-based policy making and accurate cost-benefit analyses. The module will use the potential outcomes framework and introduce directed acyclic graphs (DAGs) as a way of understanding causal pathways. Topics including instrumental variables, difference-in-differences, synthetic controls, and regression discontinuity, as well as experimental design. Students will be able to critique empirical policy research as well understand how to do policy analysis with non-experimental and experimental data. Generative AI will be incorporated into the module as an aid for coding in Python.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST TAKE PP5000

Assessment pattern

Coursework= 100%

Re-assessment

Coursework= 100%

Learning and teaching methods and delivery

Weekly contact

2 hour Lecture (x 10 weeks)

Intended learning outcomes

  • Understanding why randomized experiments are viewed as the 鈥済old standard鈥� for estimating causal effects, as well as the limitations of experiments.
  • Understand why the various methods (instrumental variables, difference-in-differences, synthetic controls, and regression discontinuity) are appropriate in different situations.
  • Use Python to estimate econometric models for policy evaluation.
  • Be able to read an empirical policy paper and understand and critique the methods used.
  • Gain experience working with data sets of various types.
  • Be able to communicate empirical results both to specialists and a more general audience.