The course introduces a range of quantitative methodological approaches for evaluating the impact of public policies and interventions. Drawing on the counterfactual framework for causal analysis, the course explores both established methods, such as natural experiments, and recent developments, including causal machine learning and administrative data. Through a combination of critical discussion and hands-on exercises, the course encourages students to assess the opportunities and challenges of applying advanced approaches to real-world policy settings, while reflecting on their practical and ethical implications.

Two hours lecture followed by one hour workshop, once per week, over 5 consecutive weeks with a tutorial.

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Students will complete an individual report (90% of the final grade) in the form of a research study protocol [1,800 words; ILOs 1, 2, 3 and 4]. Students will be allocated a policy challenge to address as part of a group. Using a template document, each student will independently develop and justify a suitable research design and data sources and provide a detailed account of how the study would be implemented.

Students will additionally work in groups to develop and deliver an oral presentation (15 minutes maximum; 10% of the final grade) [ILOs 1, 2, 3, and 4] of their group's study protocol. During the presentation, students will reflect on the rationale for selecting what they consider the most appropriate research design and data sources among those individually developed, and identify key challenges and opportunities.

The course aims to:

■ Provide students with advanced analytical knowledge and skills for evaluating the causal impact of public policies and interventions in response to increasingly complex real-world challenges;

■ Develop a critical understanding of established quantitative approaches, including natural experiments and graphical frameworks for causal analysis, alongside recent innovations such as administrative data and causal machine learning, with particular attention to how these methods identify counterfactuals and address statistical bias;

■ Develop students' capacity to critically evaluate policy effectiveness and generate rigorous evidence to support decision making in government and public policy contexts;

■ Build applied and critical research skills through engagement with real-world policy challenges, interdisciplinary case studies and the design of research study protocols through both individual and collaborative work;

■ Support students in becoming independent policy analysts who can assess methodological choices rigorously, communicate evidence effectively and contribute to evidence-informed policy development across a range of governmental and non-governmental settings.

By the end of this course students will be able to:

1. Develop a critical understanding of how advanced quantitative approaches and novel data sources can be used to address real-world policy challenges and enhance policy insight and impact

2. Demonstrate a critical appreciation of the main strengths and limitations of advanced causal approaches for policy evaluation, including their assumptions, and understand how these approaches can mitigate statistical bias

3. Develop and critically assess research study designs and methodological strategies to address complex questions of causality using counterfactual reasoning

4. Communicate policy research clearly and effectively, drawing on the assessment of rigorous study designs and methodologies to inform policy decision making