The course focuses on experimental and quasi-experimental approaches to identify and infer causal relationships in a marketing context, such as the impact of online ads on website visits or price discounts on demand. Using counterfactual reasoning when optimizing marketing actions, the course covers various methods like difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction to estimate the causal effects. Students learn how to clearly assert identifying assumptions and how to explore the behavioral mechanism of a causal effect. Lastly, the course also teaches students how firms use customer data for targeting and how to evaluate these policies.
In hands-on exercises, students will study academic marketing papers using causal inference and apply the statistical software R to reproduce/replicate the results (i.e., manage (potentially large) data sets, estimate causal effects, and communicate the findings).
Preconditions: Modules “Marketing Management” and “Applied Econometrics” are recommended
Goldfarb, A., Tucker, C., & Wang, Y. (2022). Conducting research in marketing with quasi-experiments. Journal of Marketing, 86(3), 1-20.
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.
StO/PO MA 2016: 6 LP, Modul: "Causal Inference in Marketing"
StO/PO MEMS 2016: 6 LP, Modul: "Causal Inference in Marketing", Major: Quantitative Management Science
Written exam (90 min)
Die Veranstaltung wurde 6 mal im Vorlesungsverzeichnis SoSe 2025 gefunden:
