Causal inference methods have not only come to dominate economic research but are also increasingly used by government agencies and private-sector consulting. This course introduces empirical applications of microeconometric methods, with a focus on modern causal research designs. It is built around a hands-on lab format—students read selected textbook chapters at home and then use Python in class to bring the methods to life. Rather than diving into technical detail, the course builds intuitive understanding of the causal-inference mindset and focuses on making sense of real-world data and interpreting results correctly. The following topics are covered:

• Data Operations and Visualizations.
• Anatomy of OLS.
• Hypothesis Testing.
• Fixed Effects.
• Randomized Control Trials.
• Instrumental Variables.
• Difference-in-Differences.
• Robustness Tests.

Mandatory textbooks (available online in open access): 

Huntington-Klein, Nick. The Effect: An Introduction to Research Design and Causality. (chapters 1-5, 10, 13, 16, 18, 19)

Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. (2016). Impact Evaluation in Practice. (chapter 4)

By the end of the course, students will be able to:

• Develop a solid understanding of modern causal research designs.

• Critically evaluate the findings of empirical studies.

• Analyze data in a structured and rigorous way.

• Apply econometric methods using Python through hands-on coding experience.

• Strengthen their teamwork and collaborative problem-solving skills.

This is a course with immanent examination character (PI), attendance is mandatory. Students may have a maximum of 2 absences.

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Basic background in econometrics and statistics is recommended.