Causal Inference for Business Decision Making

What it is about

You learn the foundations of causal inference for business decisions: from philosophical differences between correlation and causality to experimental design and modern methods such as instrumental variables and causal machine learning. In the end you will be able to model business problems with counterfactuals, DAGs and appropriate methods and estimate causal effects in R or Python.

What you will be able to do

• Assess philosophical distinction between correlation and causality
• Apply Counterfactual Framework and causal DAGs to business problems
• Identify and control common bias mechanisms (Confounding, Collider, Mediator)
• Estimate causal effects in R or Python
• Carry out and report a complete causal analysis project

What the module consists of

• LecturesConveying the theoretical foundations and concepts
• Coding LabsPractice-oriented application of the methods in R or Python
• Project WorkApplication of the methods in an end-to-end project
• PresentationPresentation and discussion of the project results

Teaching method

• LecturesIntroduction to theory, philosophy and methodological foundations
• Coding LabsPractical implementation: e.g. permutation tests, simulations of bias
• Project WorkDeep application: hypothesis development to reporting
• PresentationCommunication and critical reflection of the results