Dieser Kurs findet an der HU statt. Die Anmeldung erfolgt über Agnes. Alle weiteren Informationen finden Sie hier. https://agnes.hu-berlin.de/lupo/rds?state=verpublish&status=init&vmfile=no&publishid=198372&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung close

This course introduces students to the design-based approach to causal inference, combining the potential outcomes framework and DAGs. The course aims at introducing the logic and principles of causal inference, focusing on understanding the intuition of these approaches, and on students’ developing their own research ideas. Some of the topics include randomized experiments, matching, panel and difference-in-differences, instrumental variables and regression-discontinuity. We will outline of each topic, include a lab session using R and a discussion of examples from political science and economics. Prior knowledge of hypothesis testing and linear regression is required, knowledge of the statistical software R is an advantage. close

Angrist (2008). Mostly Harmless Econometrics. Princeton University Press. ?Cunningham (2021): Causal Inference: The Mixtape. Yale U ?Gerber & Green (2012). Field experiments: Design, analysis, and interpretation. WW Norton ?Morgan & Winship (2007). Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press. close