TAX 620: Causal Data Science for Business Decision Making

Most practical managerial decisions and discussions in the business sciences evolve around questions such as “What happens to Y if we change X?”, “Is the new business strategy X the reason for increases in revenue Y?”, or “Is the change that we see in Y caused by changes in X or is the change in Y driven by coincidence or some other factor?”. In other words, both practical decision-making and academic research on business decisions require knowledge about cause and effect. However, identifying causalities is usually not straightforward. For example, if a manager implements some new tax-planning strategy and the firm’s profit increases in the subsequent year, it is not clear if the new strategy was the cause for increased profits or if profits would have increased even in the absence of the new strategy. That is, the correlation between the new strategy and subsequent profits does not necessarily reflect a causal effect. A serious evaluation of the new business strategy will, however, need to identify if the change in profits was indeed caused by the new strategy.
Such an analysis of causal effects requires knowledge of both practical data analysis (using statistical software) and methods and strategies to identify causal effects. This course equips students with the skills related to both these components: it provides i) an introduction to causality and an overview of the most important methods and approaches for causal inference, and ii) a hands-on practical introduction to data analysis.
Overall, students learn how to apply the most important methods and how to use statistical software (including coding and the handling of “big data” and common business data bases) in the context of empirical work. In general, these skills are very valuable for work both in industry and academia.
The course is generally suited for students with and without prior knowledge of, or particular interest in, taxation: Examples will be from taxation, but the taught methods and empirical applications generalize beyond tax topics.
In line with the objectives of the class, one part of the course focuses on hands-on empirical applications and students learn how to conduct their own empirical analysis. For this purpose, students are introduced to the usage of a statistical software package (R or Stata) and to the access and analysis of large data sets (in particular firm databases sets such as Compustat). The introduction to software R starts from scratch and no prior knowledge is necessary.
The other part of the course teaches the concept of causality and the most important methods to estimate causal effects. These include randomized experiments, linear regression, difference-in-differences, instrumental variables, and regression discontinuity design. The focus is on an intuitive understanding of the advantages and disadvantages of the available methods, and less on a highly technical presentation.
To receive a grade, students are required to conduct an independent empirical project using statistical software and real-world data (either an own research idea or a replication of an existing research paper).

Learning outcomes

  • Hands-on practice of empirical analysis using statistical software and data.
  • Overview of most important methods and approaches for applied causal inference.

Necessary prerequisites

Recommended prerequisites
Introductory classes in statistics and/or econometrics at Bachelor level are advantageous.

Forms of teaching and learningContact hoursIndependent study time
Lecture4 SWS10 SWS
ECTS credits8
Graded yes
Form of assessmentPresentation of a database (15%), presentation of empirical project (45%), report about empirical project (40%)
Restricted admissionno
Further information
Performing lecturer
Prof. Dr. Philipp Dörrenberg
Prof. Dr. Johannes Voget
Prof. Dr. Philipp Dörrenberg
Prof. Dr. Johannes Voget
Frequency of offeringFall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., LL.M., MMM Business Research Program
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4
LiteratureJoshua D. Angrist and Jörn-Steffen Pischke, Master­ing Metrics: The Path from Cause to Effect. Princeton University Press
Course outlineData Analysis in Practice, including Introduction to Statistical Software Package “R”
Applied Introduction to Causal Inference:
Overview and Challenges of Causality
Experimental Gold-Standard
Regression and Difference-in-Difference
Instrumental Variables
Regression Discontinuity Design