• Formulate and operationalize research problems for which one or more of the methods introduced in the course is suitable
• Select the appropriate methods to best identify possible causal effects in a given research question
• After identifying the appropriate approach, conduct quantitative, empirical analysis using R and correctly interpret the results
• Discuss and account for the underlying principles behind the applied method(s), and reflect on their strengths and weaknesses
• Discuss the implications of the analytical results, possible modifications, and develop recommendations for potential policymakers, program directors, and organizations

The course introduces students to quantitative methods at an intermediate level. An initial focus of the course is to introduce students to the theoretical concepts behind causal inference. Next, the course focuses on how to design research to identify the causal effects of policy choices as well as impact / program evaluations (both public and private). The course includes a more advanced treatment of regression analysis and introductions to more advanced research methods. 

The course consists of a mix of lectures and applied statistical analysis and exercises in lab sessions. Students are expected to participate actively during lectures and exercises. For the exercises, students will be given short assignments, similar to case studies. In these assignments, the students will work in groups to apply one of the covered methods and undertake an impact evaluation to identify the causal effect of a policy or other treatment. The group work will help students to practice critical thinking and collaborating constructively. Results will be presented by one or two of the groups and discussed together in the exercise class. 

The aim of this course is to provide students with both theoretical and practical knowledge about quantitative methods such as multivariate OLS, panel data methods, and other identification strategies for causal inference at an intermediate and advanced level. The course will enable the student to be analytical with data and further develop the knowledge and skills achieved in the RDQM course.

Students will learn to understand the fundamental principles behind each of the statistical tools covered in the course and will be able to apply these to specific research problems.