• Understand the crucial role of causal knowledge for data-augmented decision-making in strategic management
• Have a precise understanding of what it means to say "correlation doesn’t imply causation"
• Critically reflect on the shortcomings of current correlation-based approaches for business analytics
• Understand the importance of management theory for causal inference
• Design, interpret, and critically evaluate experiments and experimental evidence in business and management contexts
• Design, interpret, and critically evaluate econometric analyses for causal inference in business and management contexts
• Carry out state-of-the-art causal data analyses, including experimental, quasi-experimental and econometric approaches, by themselves

Most managerial decision problems require answers to questions such as “what happens if?”, “what is the effect of X on Y?”, or “was it X that caused Y to go up?” In other words, practical business decision-making requires knowledge about cause-and-effect relationships. While standard tools such as machine learning and AI are designed for efficient pattern detection in high-dimensional settings, they are not able to distinguish causal relationships from simple correlations in the data. That means, most commonly used approaches to machine learning fall short in addressing pressing questions in business analytics and strategic management. This creates an important mismatch between the answers that current algorithms can provide and the problems that managers and strategists would like to solve. Which is why several leading companies from the tech sector and elsewhere (among them: Meta, Microsoft, Google, Amazon, Spotify, Zalando, and McKinsey) have started to heavily invest in their causal data science capabilities in recent years, with particular emphasis on large-scale field experiments and A/B testing infrastructures to support managerial decision-making.

This course will provide an introduction into the topic of causal inference with a focus on applications to practically relevant, data-driven business cases. The course is meant to be conceptual rather than technical, in order to bridge the gap between data science and management strategy, for better evidence-based decision-making. A variety of examples will be discussed that allow students to apply their newly obtained knowledge and carry out state-of-the-art causal analyses by themselves, including the design, interpretation, and critical evaluation of randomized experiments in business settings. The course will also consider situations in which firms cannot actively perform their own experiments but will have to rely on observational data collected from their ongoing business or from external sources. Analyzing observational data with the aim of uncovering causal effects requires econometric techniques such as instrumental variables, natural experiments, and regression discontinuity designs, which the course will also cover.  

By developing an overarching framework for causal data science, students will be put into the position to detect sources of confounding influence factors that threaten valid causal conclusions, understand the problem of selective measurement in data collection, and extrapolate causal knowledge across different business contexts. The course will cover several standard tools for causal inference, which are often used in empirical research in business and economics. Thus, while not a research methods course as such, this elective will nonetheless be highly relevant for students who plan to conduct a quantitative data analysis as part of their master thesis project.

• Teacher’s own research
• Methodology

• Development of research questions
• Data collection
• Analysis
• Discussion, critical reflection, modelling

Duflo, E., & Banerjee, A. (Eds.). (2017). Handbook of field experiments (Vol. 1). Elsevier.

Bandiera, O., Barankay, I., & Rasul, I. (2011). Field experiments with firms. Journal of Economic Perspectives, 25(3), 63-82.

Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American economic review, 94(4), 991-1013.

Plott, C. R., & Smith, V. L. (Eds.). (2008). Handbook of experimental economics results (Vol. 1). Elsevier.

Cunningham, S.(2021), Causal Inference – The Mixtape. Yale University Press. Available on-line at  https:/​/​mixtape.scunning.com/​