Causal Data Science with Directed Acyclic Graphs
What you'll learn
- Causal inference in data science and machine learning
- How to work with directed acylic graphs (DAG)
- Newest developments in causal AI
- Basic knowledge of probability and statistcs
- Basic programming skills would be an advantage
This course offers an introduction into causal data science with directed acyclic graphs (DAG). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach to causal reasoning. Originally developed in the computer science and artificial intelligence field, they recently gained more and more traction also in other scientific disciplines (such as, e.g., machine learning, economics, finance, health sciences, and philosophy). DAGs allow to check the validity of causal statements based on intuitive graphical criteria, that do not require any algebra. In addition, they open up the possibility to completely automatize the causal inference task with the help of special identification algorithms. As an encompassing framework for causal thinking, DAGs are becoming an essential tool for everyone interested in data science and machine learning.
The course provides a good overview of the theoretical advances that have been made in causal data science during the last thirty year. The focus lies on practical applications of the theory and students will be put into the position to apply causal data science methods in their own work. Hands-on examples, discussed in the statistical software package R, will guide through the presented material. There are no particular prerequisites for participating. However, a good working knowledge in probability and basic programming skills are a benefit.
Who this course is for:
- Data scientists
- Computer Scientists
- People intersted in machine learning
Paul Hünermund is an Assistant Professor of Strategy and Innovation at Copenhagen Business School. In his research, Dr. Hünermund studies how firms can leverage new technologies in the space of machine learning and artificial intelligence for value creation and competitive advantage. His work explores the potential for biases in organizational decision-making and ways for managers to counter them. It thereby sheds light on the origins of effective business strategies in markets characterized by a high degree of technological competition and the resulting implications for economic growth and environmental sustainability.
To study the determinants of firm innovation activities and performance, his research builds on ideas from a range of disciplines including economics, business strategy, game theory, and psychology. Furthermore, it employs a variety of methods from econometrics, machine learning, and the field of causal inference. Dr. Hünermund’s work provides insights for policymakers on how to optimally designing public R&D support schemes, which he has communicated widely in consulting projects and keynote addresses to the European Commission, the German Federal Ministry of Research and Education, and the OECD. He is the co-founder of causalscience[dot]org, a platform for fostering knowledge exchange between industry and academia on topics related to causal data science.
His research has been published in Journal of Management Studies, Research Policy, Journal of Product Innovation Management, International Journal of Industrial Organization, and Harvard Business Review, among others. Dr. Hünermund serves on the editorial board of the Journal of Causal Inference and on the executive team of the Technology and Innovation Management division at the Academy of Management. He studied economics at the University of Mannheim, HEC Lausanne, and NYU Stern School of Business, and earned a Ph.D. in business economics at KU Leuven in Belgium. His work has been covered by Frankfurter Allgemeine Zeitung, Süddeutsche Zeitung and Neue Zürcher Zeitung. In 2021, Capital Magazine voted him on its “top 40 under 40” list.