Causal Data Science
Kausale Datenanalyse
6 CPSummerEN
Prerequisites
Recommended, as written
• Basics of statistics, especially estimation and testing and simple regression analysis
Contents
• Relationships and causal graphs • Identification of a causal effect and the potential outcome model • Estimation and testing of causal effect • Matching • Regression analysis, basics and nonlinear regression • Instrumental variables • Frondoor criterion • Selection bias and correction • Interrupted time series, regression discontinuity, fuzzy regression discontinuity • Panel data and fixed effects, first difference, dif-in-dif estimation • Experiments and quasi experiments The 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 with a stronger focus on implementing data-scientific algorithms. Tutorials also apply these methods to the analysis of real-world problems with simulated and real datasets. Currently, the freely available software [R] is used 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 to generate high-quality evidence and high-quality predictions.
Assessment
• mid term examination, 45 min (35%) • end term examination, 75 min (65%)
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 29 Sep 2026Original