400022 SE Causal inference (2025S)

LECTURER: Daniel Auer
This course introduces social science PhD students to the question of causality. We will criti-
cally(!) discuss seminal, provocative, and original empirical studies on key societal questions
and get to know contemporary quantitative methods that have been introduced to allow for
causal statements. First, we establish the experimental ideal and under which conditions the
analysis of non-experimental observational data can be interpreted in a causal way. Afterwards, we will discuss different experimental and quasi-experimental approaches. During the applied sessions, we will design our own RCTs, replicate existing studies, discuss published
work, and investigate if the key assumptions for causal statements are fulfilled. These exercises will help us identify the challenges we face as researchers when advancing from correlation to
causation. By the end of this course, students will be able to:
• Understand core frameworks for causal inference and counterfactual reasoning.
• Design research to address causality in observational and experimental settings.
• Critically assess and implement statistical tools for causal inference.
• Present and defend causal statements effectively.
The course will comprise
lecture input on causality, where we learn about the theoretical concept and methodolog-
ical requirements for causal statements,
applied work, where we replicate empirical studies,
and presentations & discussions of scientific papers, focusing on design challenges.