Vrije University - Summer graduate programs
Summer Course in Statistical Methods for Causal InferenceOnline Netherlands
DURATION
2 Weeks
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
Jul 2025
TUITION FEES
EUR 1,360 / per course *
STUDY FORMAT
On-Campus
* Students, PhD candidates and employees of VU Amsterdam, Amsterdam UMC or an Aurora Network Partner €765 Students and PhD candidates at partner universities of VU Amsterdam €1035 Students and PhD candidates at non-partner universities of VU Amsterdam
Key Summary
Introduction
There is great interest among students and practitioners today to understand the causal mechanisms underlying major events. Identifying cause-and-effect relationships is important for impact evaluation and effective policy design. Such identification can help us answer questions like: "What causes an economic downturn?", "Does universal basic income reduce unemployment?" and "Does a carbon tax reduce greenhouse gas emissions?"
However, identifying causal relationships using data is often error-prone. Differentiating causality from simple correlation requires learning and applying sophisticated quantitative tools. The golden standard of identifying causal linkages relies on designing experiments, often through randomized control trials. However, designing a randomized control trial is not always feasible or ethical. Moreover, some events might have already happened in the past, such as a financial crisis or a cyclone. How can one use observational data to analyze the causal effects of such events?
This course provides a hands-on introduction to statistical methods for causal inference. Over two weeks, students are introduced to experimental and quasi-experimental methods which allow them to infer cause-and-effect relationships robustly. We teach these methods from both a theoretical and applied lens, supplementing lectures with hands-on computer tutorials in the R programming language to help students learn by doing.
Course Overview
- Course level: Master's and PhD
- Lecturers: Dr. Sanchayan Banerjee & Jack Fitzgerald
- Forms of tuition: Lectures and computer tutorials
- Forms of assessment: A final project (60%) and daily quizzes (40%)
- Contact hours: 45
Admissions
Scholarships and Funding
Equal Access Scholarship
Application Procedure
Application for the Equal Access Scholarship will open in Febraury
Great that you are interested in applying for the Equal Access Scholarship. You can apply to the scholarship between 12 February and 1 April. Please be aware that it is only possible to select one course.
The results of the scholarship selection will be announced in May. Since we have a limited number of scholarships available for a large number of applicants, we suggest - if possible! - to complete your payment at the time of your course application to guarantee your place in the course. However, if you are not able to come without the scholarship, you can just wait until the announcement. If you would like to come, regardless of whether you will be granted the scholarship, it is best to secure your place in the course by completing your payment via our regular application form. If the scholarship is granted to you, the tuition and accommodation fees will be reimbursed.
Deadline to submit your Equal Access Scholarship application: 31 March (23:59 CET).
Requirements
When you apply via the Equal Access Scholarship application form you will be requested to upload the following documents:
- Curriculum Vitae/Résumé (CV) stating your educational background.
- Professional Letter of Reference Including:
- His/her/their experience working with you (either in an academic, professional, or volunteer setting)
- His/her/their motivation for recommending you for the scholarship
- Complete contact information
- His/her/their experience working with you (either in an academic, professional, or volunteer setting)
- His/her/their motivation for recommending you for the scholarship
- Complete contact information
- When filling out the scholarship form, we will ask the following questions*:
- Why are you interested in joining VU Amsterdam Summer School?
- What’s your motivation for selecting this course?
- How you will use the information you learn to make a positive impact in the future for both you and your community?
- Why do you deserve this scholarship?
- Why are you interested in joining VU Amsterdam Summer School?
- What’s your motivation for selecting this course?
- How you will use the information you learn to make a positive impact in the future for both you and your community?
- Why do you deserve this scholarship?
Please stick to a maximum of 150 words per question.
Green Travel Grant
At VU Amsterdam Summer School we are also committed to VU's sustainability goals and we aim to reduce the environmental impact of mobility, and specifically, student travel. Therefore, we are thrilled to offer Green Travel Grants to encourage sustainable travel for students attending our summer school.
Where can I apply?
Once the courses have been confirmed to run in mid-May or June, we will send out a newsletter to our participants with a link where they can apply for either funding for train travel or funding for bus travel.
The application period will last two weeks, and we will select the winners via a lottery system. More information on the specific deadlines can be found in the newsletter we send out in May.
How does it work?
For students to receive the economic compensation they will need to submit their purchased travel tickets via email within two weeks after being selected as winners of the grant. Once the deadline to submit their tickets has passed, the students will receive the reimbursement.
Curriculum
In week one, students are introduced to the pitfalls of standard regression analysis by identifying multiple threats to causality, such as omitted variable bias, endogeneity concerns like simultaneity bias, and reverse causality problems. Then, they are introduced to the potential outcomes framework (Neyman, 1923; Rubin, 1977), a standard workhorse model of statistics, that forms the basis for identifying cause-and-effect relationships. Following this, they learn how to design experiments and analyze experimental data for impact evaluation and policy analysis. Finally, at the end of the first week, students are introduced to quantitative methods of causal inference for observational data, where they are taught to select observables to create treatment and control units using matching analysis.
In week two, students continue to learn four more methods to evaluate quasi-experimental phenomena (so-called “natural” experiments). Here, we start with instrumental variables regression, including a guest lecture by Professor Hans Koster on the use of instrumental variables to estimate the impact of historical monument refurbishment on Dutch housing prices. Following this, students are introduced to panel data designs with difference-in-differences estimation. Finally, students are introduced to regression discontinuity designs.
All lectures are complemented with hands-on computer tutorials, where students learn how to apply these quantitative methods of causal inference using R.
Day 1
The Potential Outcomes Framework
Day 2
RCTs and Matching
Day 3
Panel Data Models
Day 4
Differences in Differences
Day 5
Instrumental Variables Estimation
Day 6
Regression Discontinuity Design
Day 7
Power in Causal Inference
Day 8
Advanced Experimental Design
Day 9
Exam Review
Day 10
Final Matters
Program Outcome
By the end of this course, students will be able to:
- Understand the difference between correlation and causation.
- Apply quantitative methods of statistical data analysis to infer causal relationships.
- Identify confounding factors that threaten causal inference and hamper the internal and external validity of analytical findings.
- Critically analyze data using statistical methods like experiments, matching analysis, difference-in-differences, regression discontinuity, and instrumental variables estimation.
- Explore challenges and limitations in the use of quantitative methods of causal inference such as data availability, missing data, and measurement errors.
- Apply diagnostic knowledge to inform impact evaluations and develop evidence-based policies