English   Danish

2023/2024  KAN-CPOLO2050U  Advanced Quantitative Methods

English Title
Advanced Quantitative Methods

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory (also offered as elective)
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc i International Business and Politics, MSc
Course coordinator
  • Benjamin Carl Krag Egerod - Department of International Economics, Goverment and Business (EGB)
Main academic disciplines
  • Political Science
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 07-09-2023

Relevant links

Learning objectives
  • Articulate the fundamental problem of causal inference.
  • Explain how various research designs seek to draw causal inference.
  • Present a substantive discussion of how the assumptions of a research design can be violated in a specific setting.
  • Use statistical software to conduct an independent empirical analysis of a problem using the research designs and methods introduced in the course.
  • Interpret and analyse the results of analyses appropriately in relation to a given research problem.
  • Critically evaluate the strengths and weaknesses of the research designs and methods introduced in the course in their application to a given research problem.
  • Formulate and justify solutions for actors based on the analysis.
Course prerequisites
While we will recap core concepts, basic knowledge of linear regression, statistical uncertainty and descriptive statistics will be presumed. The course will rely on the statistical software R, but no prior knowledge of the software is assumed.
The course shares exams with
Course content, structure and pedagogical approach

In business, public policy and the non-governmental sector, the demand for “evidence” is stronger than ever. Managers and policy-makers place an increasing premium on knowing “what works”, when they decide on which direction to take the company or the country. Additionally, recent decades have seen an explosion of data availability. Combined with advances in causal inference, this puts social scientists in a better position to answer the demand for evidence than they ever have been.


This course provides a case-based introduction to techniques that allow us not only to measure which policies “work”, but also to quantify how much they work. That is, drawing causal inferences. We will cover four broader areas:


1) The so-called potential outcomes framework for causal inference.

2) Research designs that allow for drawing causal inferences.

3) The statistical implementation in the software R.

4) Synthesizing research results and using them for policy recommendations.


The course will be taught as a mix of lectures in larger groups and exercise classes in smaller groups. The teaching format is “particular general particular”. We will introduce a particular question, then discuss how such a question is handled in general by reviewing core concepts from the literature, and we then return to the particular application by focusing implementation and extensions.


Throughout the course, we will follow an applied, hands-on approach, emphasizing the implementation and interpretation of statistical analyses. Hence, students will spend a substantial amount of time working with software. The exercise classes will be based on group work prior to class. Solutions and implementations will then be discussed in class. The course software will be R. All activities will be based on data from actual research projects.


In relation to Nordic Nine

The course supports the Nordic Nine (NN) capabilities by training analytic skills and critical thinking through hands-on work with data. In the lectures, students will learn fundamental concepts from statistics and causal inference. In the exercises, they will apply these concepts by criticizing a published piece research (NN6), discussing constructively how to improve its research design (NN6), and honing data analytic skills by replicating the results with real-world data (NN2). This will form the basis of class discussions, where we will deliberate about how a particular piece of research informs us about society (NN1), and helps resolve the challenges facing us (NN3). 

Description of the teaching methods
The course will be taught in-person. The teaching in the physical classroom will be supplemented by pre-recorded videos introducing and elaborating on aspects of the syllabus. For example, step-by-step coding introductions will be recorded and made available for the students to revisit as they work on their exercises.
Feedback during the teaching period
AQM is a workshop-based course, and students will receive feedback continuously during workshop classes. Throughout the sessions, personal feedback is provided in-class by means of the exercise sessions.

In particular, all lectures are followed by exercise sessions. Here, students will be provided with feedback on the assignments they have prepared before the class meets. Workshop instructors will walk through the exercise with the students, thereby providing additional group feedback.

Besides this, students are welcome to make use of the instructors’ weekly office hours.
Student workload
Lectures, exercises and workshops 48 hours
Course preparation. Includes: readings for lectures and exercises work on activities (homeworks) 80 hours
Exam 80 hours
Last updated on 07-09-2023