22-30.107 Causal Machine Learning

Instructors: Prof. Dr. Jan Marcus

Event type: Interactive class

Displayed in timetable as:

Hours per week: 3

Credits: 6,0

Language of instruction: English

Min. | Max. participants: - | 45

Comments/contents:
The course consists of three main parts.The first part provides an introduction to causality and the counterfactual framework, while the second part deals with basics in supervised machine learning. The third part combines the first two parts by discussing how machine learning can contribute to the identification of causal effects.


Structure

1. Causality

• The counterfactual framework
  
• Causal graphs (DAGs)
  
• Designs that control for confounding
  

• General introduction
  
• Shrinkage methods
  
• Tree-based methods
  

• Homogenous effects
  


• Post-double-selection
  
• Double-Machine-Learning
  


• Heterogeneous effects
  


• Group Average Treatment Effects (GATEs)
  
• Causal trees/forests
  

• Student presentation
  
• Active participation
  
• Seminar paper