Statistical Methods for Causal Inference, Short Course | Vrije Universiteit Amsterdam | Amsterdam, Netherlands
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Statistical Methods for Causal Inference

8 days
1050 EUR/full
1050 EUR/full
Tuition fee
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This Statistical Methods for Causal Inference course from Vrije Universiteit Amsterdam provides a hands-on introduction to statistical methods for causal inference.


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 randomised control trials. But designing a randomised 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 analyse the causal effects of such events?

Over two weeks, students in the Statistical Methods for Causal Inference course from Vrije Universiteit Amsterdam are introduced to experimental and quasi-experimental methods which allow them to infer cause-and-effect relationships robustly.

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 analyse 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

Programme Structure

  • In week one, students are introduced to 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 of identifying cause-and-effect relationships. 
  • 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. 

Key information


  • Full-time
    • 8 days

Start dates & application deadlines


TOEFL admission requirements TOEFL® IBT




On Campus

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

TOEFL admission requirements TOEFL® IBT

Other requirements

General requirements

  • This course is on Master/PhD level but open to Advanced Bachelor's students and working professionals, across all disciplines in quantitative social sciences. These include business, criminology, economics, econometrics, education, environmental sciences, finance, health sciences, international studies, psychology, public policy, political science, social policy, sociology, and statistics, all broadly defined. 
  • Participating students are expected to have prior knowledge of regression analysis and hypothesis testing. If you do not have this knowledge, you can still participate in this course by additionally following the VU Amsterdam Summer School course Data Analysis in R in a previous session. Prior coding experience specifically in R is preferred but is not a prerequisite of the course.

Tuition Fee

To always see correct tuition fees
  • International

    1050 EUR/full
    Tuition Fee
    Based on the tuition of 1050 EUR for the full programme during 8 days.
  • EU/EEA

    1050 EUR/full
    Tuition Fee
    Based on the tuition of 1050 EUR for the full programme during 8 days.
  • Students, PhD students and employees of VU Amsterdam, Amsterdam UMC or an Aurora Network Partner: €700
    • Students at Partner Universities of VU Amsterdam: €950
    • Students and PhD candidates at non-partner universities of VU Amsterdam: €1050
    • Professionals: €1250

Living costs for Amsterdam

1000 - 1500 EUR /month
Living costs

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.


Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

Our partners

Statistical Methods for Causal Inference
Vrije Universiteit Amsterdam
Statistical Methods for Causal Inference
Vrije Universiteit Amsterdam


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