Causal inference methods for policy evaluation

Course goals

The objective of this course is that the student acquires knowledge of causal inference methods developed in econometrics. At the end of the course the student should be able to:

• understand the difference between correlation and causation
• develop strategies for identifying causal relationships
• assess the validity of empirical studies on cause-effect relationships
• apply causal inference methods to identify causal effects in R
• interpret and evaluate the results from causal inference methods
• explain such evaluations in layman’s terms

Assessment
The final grade is made up of 2 exams:

• individual written midterm exam (50% of the final grade).
• individual written final exam (50% as well).

Content

Causal inference is the process of drawing conclusions about the effects of interventions or treatments on outcomes of interest. Policy evaluation is the application of causal inference methods to assess the impact and effectiveness of policies or programs.

This course introduces the main concepts and methods of causal inference and policy evaluation, such as potential outcomes, observational studies, matching, propensity score, regression discontinuity, instrumental variables, difference-in-differences, and synthetic control.
Aim is to equip students with the skills and tools to critically evaluate existing policy studies and to design and conduct their own policy analysis using real-world data.

The course is suitable for students who have some background in statistics and econometrics and who are interested in applying causal inference methods to policy questions in various fields, such as education, health, labor, environment, and development.

During this course, students actively learn how to design and assess causal identification strategies, how to apply the methods in the statistical software R, and how to interpret and evaluate the results of these analyses.
The course has a strong focus on designing and assessing identification strategies, as well as on actively applying these methods in R. Students gain hands-on experience in both, discussing the validity of identification strategies as well as in applying the methods.

Course form
Lectures (mandatory) and practical sessions (optional).
Weekly assignments will focus on both the applications of the discussed techniques and theoretical questions.
Students work independently on the weekly assignments and have a weekly opportunity to get feedback and assistance in the practical sessions.

Additional information

Enrolment in this course is only available by using the pre-enrolment form that will be forwarded to all ADSM students in the first week of November.