Focus of the Digital Causality Labs
In a world driven by data, it is becoming increasingly important to adequately assess cause-and-effect relationships. Methods and tools from the field of causal inference help to examine empirical relationships with regard to their causality. Given the increasing amount of (mis)information, it is essential to impart causal data literacy skills: With the right knowledge, studies can be classified and critically examined. Students should be able to recognize systematic biases, for example, due to background variables (“confounders”) or sample selection, and assess their implications for data analysis and interpretation.
Assessing causal relationships is an essential step in making appropriate and targeted decisions – not only in a private or professional context, but also with regard to social expertise. As part of this second funding phase of the project, the communication of causal knowledge has been opened up to a broader audience.
Review and results
With the help of the funding, a modern, interesting, and diverse course was developed and delivered. As part of the project, a new, low-threshold introductory course on methods and tools of causal inference was created. New teaching materials were created for this course, based on current books, data examples, and supplementary content. The focus was on the intuitive teaching of causal modeling. Formal and mathematical content was taught on this basis. Furthermore, high-quality instructional videos were recorded and produced, which were embedded in an online course. The newly developed course replaces a previous subject-specific introductory course in business administration on the topic of causal inference. The didactic concepts and interactive learning apps developed in the first project phase were integrated into the new interdisciplinary course. The course will remain open to students of all subjects in the future as part of the free elective area.
In the context of their own project work, the students were able to give free rein to their creativity and independently apply the skills acquired in the lecture. The developed data products reflect the diverse interests of the students. They cover topics related to statistical methods of causal inference, statistical software, and more recent topics such as causality and artificial intelligence. The source code and the various development phases are accessible via publicly accessible GitHub repositories. A gallery of student projects can be found on the Digital Causality Lab website.
Tips from lecturers for lecturers
Hybrid teaching was a key success factor for the course, particularly through the conception and production of learning videos and their embedding in a high-quality online course. A great deal of valuable experience was also gained through practical implementation and combination with regular face-to-face teaching. The course builds on the DCL project in the second funding round, which explored new didactic approaches based on inquiry-based learning. Therefore, the implementation of inquiry-based learning in teaching was also improved during this funding phase, which will benefit this and other courses in the long term. Key to this was not only the comparison with the set and actually achieved teaching goals, but also the exchange with the students. The participants themselves were very interested and wanted to contribute to improving teaching.