QMCI
MBR Program (A/I course)
WS 2024/2025
Course description
The course will provide PhD students with a comprehensive understanding of contemporary causal inference techniques. Focusing on quasi-experimental methods like Difference-in-Differences, Regression Discontinuity Design, and Synthetic Control Methods, the course will emphasize both theoretical foundations and practical applications. Participants will engage in hands-on empirical exercises relying on datasets from published papers from Economics and Management. The course aims to enhance students' ability to conduct robust causal analysis in their research. Stata will be the software used for examples and solutions. However, participants can use Python or R at their convenience during the hands-on session and the exam. Prior completion of the course “Quantitative Methods” is required to participate in the course.
Course material
Lecture slides and data files will be made available on Moodle before each session. After each session, suggested solutions to empirical exercises will be provided (only Stata code). All material will be in English.
Prerequisites
The course is open to PhD students currently enrolled in the MBR program and is credited as an A/I course. Students must have participated to the course Quantitative Methods before participating to QMCI. Participants to the course should feel comfortable working with Stata – or be proficient in Python or R if they opt for working with alternative softwares.
Organization of the sessions
The course will be organized into four sessions of five and a half hours (22 hours in total). Sessions are held in person at Luk-Pool, Ludwigstr. 28 VG, 2.Stock, Raum 207.
Attendance at all teaching sessions is mandatory. If you have to leave earlier or arrive later one of the days, please write in advance to let me know.
Examination
The exam will take the form of an exam of 3 hours which will combine:
a set of theoretical questions
o 50% of the grade.
o No books, no notes, or computer will be allowed.
an empirical “open book” exercise similar to one of the exercises solved during class
o 50% of the grade.
o Students will use the software of their choice to perform this exercise.
o Code, figures and tables produced by the students will be evaluated.
Two exam dates are offered (students have to choose one): 14.02.2025 or 21.02.2025, 08:00 -12:00. No alternative dates or examination type (e.g., homework) will be offered. If you cannot take the exam, please do not register for the course.
Course structure
The course is organized around six topics.
Each topic will combine:
a theoretical section where we will present the setting, the assumptions, the properties of estimators, as well as the pros and cons of approaches (120 minutes)
an applied section where examples from one or several published papers will be presented and discussed (30 minutes).
an exercise relying on a dataset, solved by the course participants (90 min).
o Solutions (in Stata) will be provided at the end of the session.
Outline of the course
1. Course presentation: outline, organization, examination
2. Introduction and overview of the methods
3. Matching methods
4. Difference in Differences – advanced tools
5. Synthetic Control
6. Regression Discontinuity Design
Main references
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using Stata. Stata press.
Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics using Stata: Volume 2 Non Linear Models and Causal Inference Methods. Stata press.
Cunningham, S. (2020). Causal Inference. The Mixtape, 1.
Huntington-Klein, N. (2021). The effect : An introduction to research design and causality.