Causal Data Science in Business and Economics
Kausale Datenanalyse in den Wirtschaftswissenschaften
6 CPSummerENno longer offered
Prerequisites
Recommended, as written
• basics of statistics, especially estimation and testing and simple regression analysis
Contents
• Counterfactuals, Potential Outcomes, Causal Graphs, and typical problems (i.e., omitted relevant variables, measurement error, reverse causality, endogenous selection, endogenous treatment) • Data analytic solutions: control variables, matching, weighting • Data analytic solutions: instrumental variables, selection instruments • Data collection solutions: real experiments • Assumed experiments as mixed solutions: natural experiments, quasi experiments, regression discontinuity • Times series data as mixed solution: diff-in-diff and related methods • Reflections on moderation and mediation analyses respectively structural equation modelingThe module focuses on applications in business and economics, but the underlying theories and methods generalize beyond these fields. The course complements more traditional data science modules that have a stronger focus on the implementation of data scientific algorithms. Tutorials also apply these methods to the analysis of real-world problems and the analysis of simulated and real datasets. Currently, the freely available software [R] is uses in the practical parts of the tutorials.
Learning outcomes
Students have a basic understanding of data science in the context of the identification of causal relationships. They are familiar with a verbal, and graphical language to communicate about causality, and with key concepts, such as counterfactuals, outcome equivalence, and confounding effects. They know about typical classes of problems that do not allow causal interpretations of observed associations as well as typical solutions for these problems by means of data analytic and data collection methods. Moreover, students understand the tight interdependency of data analytics and the design of data collection in order to generate high quality evidence and high quality predictions.
Assessment
• 3 written partical examinations, 3x30 min, (each weighted 1/3)
Literature (4)
- Pearl, J. (2009). Causality. Cambridge University Press,
- Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference. Methods and Principles for Social Research. Cambridge University Press.
- Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton university press.
- Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton.
Remarks
Tutorials are open to questions in English and German.
Source: BTU's module description · retrieved 18 Oct 2026Original