Causal Inference


Causal identification is critical to the testing of theoretical relationships in organizational science. This course is designed to provide an understanding of methodological concepts as well as skill in apply various analytical techniques for causal identification. As such, the main objective of this course is for student to develop the research skills related to causal inference and identification.  


The course will cover the following contents: Potential outcome framework, randomized experiments, matching, difference in differences, regression discontinuity design, and instrumental variables.

The information provided here is under regular reservation. When you sign up, please check Canvas for the most-up-to-date information.


80% written assignment: This course requires students to write a short empirical paper on a research topic of their choice. This paper should apply at least some of the methods in the course to an empirical problem. It should be 5-15 pages and focus on the research design, data, methodology, analysis, and results.

20% contribution to classroom discussion.



Angrist, Joshua D. and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.

Scott Cunningham. 2021. Causal Inference: The Mixtape. Yale University Press.

Research articles:

Carefully selected academic articles, to be shared on Canvas.

Additional info

The timetable for this course can be found here. (The linked timetable might not show all the sessions at one glance. Please scroll per month to see the schedule of the entire course.)

ERIM PhD candidates (Fulltime & Part-time) can register for this course via Osiris Student.

External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to the ERIM Doctoral Office by four weeks prior to the start of the course. For external participants, the course fee is 1300 euro.