Digital Causality Lab —Tracing the "why" in data

Course type: Lecture and exercise
Institution: Faculty of Business Administration
Funding period: 01.06.2022 to 31.05.2023
Short title: Digital Causality Lab

Extract from the funding application: "The definition of data literacy can be directly linked to drawing causal conclusions from data: Targeted recommendations for action can only be derived on the basis of a valid, comprehensible and critical causal analysis."

Orientation of the Digital Causality Lab

Causal inference can be a lot of fun, and students should be taught this. In addition, the methods for analyzing causal relationships are much easier to understand if they are applied independently. The theoretical approaches are always used for practical application. This is how the idea of creating a causal lab was born, in which students have the opportunity to try things out, collect ideas and, if necessary, discard them and ultimately come up with their own solutions.

UHH/Bach

Review and results

In the course of the project, a modern and innovative course in the context of causality was created. The lecture was supplemented by interactive learning materials (learning apps), which contributed to a better understanding of the theoretical content. With the Digital Causality Lab, which replaces the previous frontally taught exercise, an innovative learning space with a didactic focus on research-based learning was also established. On the one hand, important practical tools and skills of data literacy are taught in the Digital Causality Lab, and the theoretical concepts from the lecture are applied in practice. On the other hand, students deepen this knowledge in the course of causal case studies in which they independently develop a data product.

The course "Causal Inference and Digital Causality Lab" will become an integral part of the curriculum (B.Sc. Business Administration and related degree programs) and will be offered regularly as a hybrid course in the summer semester. We will also offer a fully digital version of the course (MOOC). This will then be aimed at a broad audience as part of the Studium Generale.

Added value was also created for future teaching projects. On the one hand, new didactic concepts were developed and tested and, on the other, the topic of data literacy was established as a dedicated teaching focus for the first time. In addition, many learning materials were created and shared online. An important part of the project is based on open source software development, which means that the source code for numerous learning materials (e.g. the learning apps) is freely available.

Tips from teachers for teachers

The topic of data literacy will generally have to be given greater importance in future courses. Frontal teaching scenarios are often not suitable for conveying learning content and skills. In future, an interactive and collaborative teaching approach and elements of research-based learning must be used more frequently. In addition, interactive learning apps should be used more frequently as teaching materials in statistics courses in the future. The project has had very good experiences with them in teaching.

Persons involved

Dr. Philipp Bach

Research assistant

Focus points:

Estimation of causal effects and inference in high dimensionsal settings with machine learning methods
Machine Learning and Deep Learning
Applications in labor, health and financial economics

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Gangli Tan

Doctoral Researcher

Focus points:

Digital Products / Interaction between Hardware and Software Component Development in Digital Innovation
Digital Transformations / Cloud Software for Causal AI

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