| Dozent/in |
Paloma Abril Poncela, MA |
| Veranstaltungsart |
Masterseminar |
| Code |
HS251634 |
| Semester |
Herbstsemester 2025 |
| Durchführender Fachbereich |
Politikwissenschaft |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Mi, 24.09.2025, 12:15 - 14:00 Uhr, HS 5
Fr, 28.11.2025, 09:15 - 17:00 Uhr, 3.B01
Sa, 29.11.2025, 09:15 - 15:30 Uhr, 3.B01
Fr, 05.12.2025, 09:15 - 17:00 Uhr, 3.B01
Sa, 06.12.2025, 09:15 - 15:30 Uhr, 3.B01 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
Blockveranstaltung |
| Inhalt |
This course provides a practical, applied introduction to causal inference, focusing on how researchers estimate causal effects using observational data. The goal is to help students understand, critically evaluate, and implement causal inference techniques in their own research.
Understanding causal inference is essential for drawing valid conclusions about the effects of policies, interventions, and treatments. In social sciences, policy-making, and economics, it is crucial to distinguish between mere correlations and true causal relationships. Poorly designed studies or incorrect identification strategies can lead to misleading conclusions, which may have significant consequences in real-world decision-making.
This course emphasizes the importance of strong identification strategies—the backbone of credible causal inference. Students will learn how to structure their research designs to minimize bias, control for confounders, and appropriately interpret findings. We will explore key methods such as randomized control trials (RCTs), instrumental variables (IV), difference-in-differences (DiD), and regression discontinuity designs (RDD), providing hands-on applications in political science and related fields.
By the end of the course, students will be equipped to critically assess empirical research, implement causal inference methods in their own work, and effectively communicate their findings. This course prioritizes practical application over mathematical derivations, making it accessible for students with a basic understanding of statistics while still offering valuable insights to those with more advanced training.
|
| Lernziele |
1. Explain the logic, assumptions, and applications of RCTs, IV, DiD, and RDD.
2. Critically evaluate empirical studies using causal methods, assessing strengths and limitations.
3. Design a research question and justify an appropriate causal inference strategy.
4. Implement causal methods (RCT, IV, DiD, RDD) in R and interpret results.
5. Communicate causal findings clearly, including methodological trade-offs. |
| Voraussetzungen |
Basic Statistics (OLS regression) and Basic R (very basic knowledge).
|
| Sprache |
Englisch |
| Begrenzung |
Master seminar open for advanced Bachelor and for Master students. |
| Anmeldung |
***Wichtig*** Um Credits zu erwerben ist die Anmeldung zur Lehrveranstaltung über das UniPortal zwingend erforderlich. Die Anmeldung ist ab zwei Wochen vor bis zwei Wochen nach Beginn des Semesters möglich. An- und Abmeldungen sind nach diesem Zeitraum nicht mehr möglich. Die genauen Anmeldedaten finden Sie hier: http://www.unilu.ch/ksf/semesterdaten |
| Prüfung |
No exam.
• Active Participation (20%) – Engaging in discussions and critiques.
• Final Research Proposal (40%) – A 2,500-word proposal applying a causal inference method
to a research question.
• Presentation of Research Question (40%) – Clarity, method justification, and feasibility. / 4 Credits |
| Abschlussform / Credits |
Aktive Teilnahme (Essay) / 4 Credits
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
Paloma.abrilponcela@eui.eu |
| Literatur |
1) Cunningham, Scott. (2021). Causal Inference: The Mixtape. Yale University Press. Available Online
2) Gerber, Alan S., & Green, Donald P. (2000). “The Effects of Canvassing, Telephone Calls,and Direct Mail on Voter Turnout: A Field Experiment.” American Political Science Review, 94(3), 653-663.
3) Miguel, Edward, & Kremer, Michael. (2004). “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities.” Econometrica, 72(1), 159-217.
4) Acemoglu, Daron, Johnson, Simon, & Robinson, James A. (2001). “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review, 91(5),1369-1401
5) Card, David, & Krueger, Alan B. (1994). “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania.” American Economic Review, 84(4), 772-793.
6) Eggers, Andrew C., Folke, Olle, Fowler, Anthony, & Hainmueller, Jens. (2015). “On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from Over 40,000 Close Races.” American Journal of Political Science, 59(1), 259-274.
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