This course introduces PhD students to methodological aspects of causal inference. In particular, we shall study the following topics:

• the potential outcome framework and targets such as ATE, CATE and related quantities
• randomized experiments, uncounfoundedness, propensity score
• doubly robust methods
• matching, balancing, stratification
• regression discontinuity designs
• diff-in-diff

After successfully completing the course, students should:

• be able to formulate, motivate, explain, and critically assess the methods discussed;
• understand the formal settings and assumptions underlying the methods;
• be able to read and comprehend articles in the current statistics/econometrics literature dealing with causal inference methods.

For this course participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

This course is taught as lectures with assignments and student presentations. In combination with the lectures, the assignments and the presentations help students to consolidate and expand their understanding of the methods discussed in the lectures.

Grading is based on assignments (5 assignments à 12%) and a presentation (40 %).

• 1 if final score >= 0.875
• 2 if final score < 0.875 and >=0.75
• 3 if final score < 0.75 and >=0.625
• 4 if final score < 0.625 and >=0.5
• 5 if final score < 0.5

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To follow the course, a good understanding of econometrics/statistics and probability (CLT, LLN) is necessary.