More Advanced Econometrics 2400-ZEWW1036
The course introduces students to modern causal inference methods combining theoretical foundations with practical applications. Students will explore statistical methods, software tools, and the organizational context of causal inference methods. The program emphasizes both methodological rigor and the professional realities of causal inference.
Course Outline
Basics of regression and panel data models
Potential Outcome Framework; Law of Large Numbers;
Randomized Controlled Trial; A/B Testing
Treatment Effects: Regression Adjustment; Inverse Probability Weighting; Doubly Robust Estimators; Propensity Score Matching; Mediators
Difference-in-Differences, the 2-by-2 case
Difference-in-Differences, more periods; common intervention time; parallel trend assumption; event study
Difference-in-Difference-in-Differences (DDD)
Heterogeneity in Difference-in-differences
Extended Two-Way Fixed Effects; Two-Way Mudlak Regression
Staggered Design in Difference-in-Differences
Synthetic Control Method
Doubly Robust estimators for Difference-in-Differences
Non-linear Difference-in-Differences
Student Workload Estimate
Exercises: 30h
Consultations: 1h
Preparation for exercises: 14h
Preparation of the final project: 20h
Preparation of homeworks: 10h
Total: 75h (31 contact hours + 44 independent study hours)
Course coordinators
Type of course
Learning outcomes
Learning Outcomes
A) Knowledge
Understands the causal inference methods. Knows the advantages and limitations of methods used in causal inference. Understands fundamental techniques and tools for evaluating the effectiveness of treatments (policies, trainings, etc.).
B) Skills
Can use statistical and econometric software for causal inference. Able to perform causal inference analysis with basic statistical and econometric tools. Can apply appropriate research methods to assess treatment effects. Can use functions and scripts prepared by other researchers and analysts. Can select analytical tools to solve problems in causal inference. Can perform computational and analytical operations to prepare causal inference reports.
Interprets results and prepares analytical reports.
C) Social Competences
Is prepared for continuous learning and skill development. Is prepared for communication data effectively using tables and charts. Is prepared for independent knowledge expansion. Is prepared for collaboration with existing programs and develops tools usable by others in causal inference. Is prepared for evaluation the applicability of selected tools to specific problems. Is prepared for understand the limitations of IT techniques in complex causal inference studies.
SU05, SU06, SK01, SK03, SU04, SU03, SU02, SU01, SW03, SW02, SW01, SW04, SW05, SK02, SK04
Assessment criteria
Assessment Methods
Class attendance and in-class work (20%)
Homeworks (40%)
Project (40%)
Regular attendance is mandatory. A maximum of two absences are permitted. Students must successfully complete all classes and pass the associated final project. Consistent completion of all assigned homework is required. To pass the class, students must earn at least 50% of the total points available.
Grade scale:
[0%-50%) – unsatisfactory
[50%-60%) – satisfactory
[60%-70%) – satisfactory +
[70%-80%) – good
[80%-90%) – good +
[90%-100%] – very good.