Design, Causality and Modelling

The course provides an applied introduction to the concepts, tools, and methodologies used to study causality in empirical research. Central to the course are causal diagrams (directed acyclic graphs) as a visual and conceptual aid to represent causal relationships and guide identification strategies. Students will study the back-door and front-door criteria for controlling confounding and learn about practical estimation techniques such as matching, difference-in-differences, instrumental variables, and analysis of variance (ANOVA) in the context of treatment effect estimation. Applications and examples will draw from economics, social sciences, public policy, and the life sciences.

This course introduces students to the fundamental principles of designing empirical studies that aim to uncover causal mechanisms in the world. Emphasis is placed on how thoughtful experimental and quasi-experimental design is essential for drawing valid conclusions about cause-and-effect relationships. Students will develop an understanding of how research questions about interventions, treatments, or policies can be framed and answered using data, models, and design strategies that support causal inference.

In presence

The course combines lectures with interactive tutorials and practical exercises. Students will engage with both conceptual material (e.g., causal reasoning using diagrams) and applied work involving real or simulated data to estimate treatment effects. Short in-class experiments and guided replication of published studies will be used to reinforce key concepts. Emphasis is placed on active participation, critical reflection on assumptions, and collaborative problem-solving.

The course is evaluated by means of a midterm exam and a final exam that make up 40% and 60% of the final grade, respectively.