Prof. Dr. h.c. mult. Hasso Plattner

Causal Inference - Theory and Applications in Enterprise Computing

General Information

  • Teaching staff: Dr. Matthias Uflacker, Johannes Huegle, Christopher Schmidt
  • 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Time: Tue and Wed 9.15 - 10.45
  • Room: D-E.9/10
  • First course: April 9, 2019

Short Description

The questions that motivate most data analysis are not associational but causal in nature. What are the causes and what are the effects of events under observation? Nevertheless, the statistical methods commonly used today to answer those questions are of associational nature. But “Correlation does not imply causation!”, and misinterpretation often results in incorrect deduction. 

In the recent years, causality has grown from a nebulous concept into a mathematical theory. This is largely due to the work of Judea Pearl (2009) who has received the 2011 Turing Award for “fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”.

In this lecture, you look at the mathematical concepts that build the basis of causal inference and how they can be used to derive causal relationships from observational data. After you gained an overview on the needed concepts, you will work on particular tasks in the context of the causal inference procedure. Moreover, you will present your work and write a scientific report about your implementation results.

Goals of the Course

  • Understand...
    • opportunities of causal inference
    • the mathematical concepts 
    • challenges and problems in the context of real-world applications
    • constraint-based algorithms to derive causal relationships
  • Do...
    • work in small teams
    • work on a specific topic in the context of data-driven causal inference
    • implement algorithms and analyze performance results
    • write a scientific report
  • Improve...
    • mathematical, analytical, and modeling skills
    • scientific working and writing
    • machine learning techniques


  • the participants should be interested in mathematical methods


  • 50 % Implementation and presentation of results
  • 40% Scientific report
  • 10% Personal engagement

Dates & Topics

09.04.2019Lecture Overview and Causal Inference in a NutshellCausal_Inference-Theory_and_Applications
10.04.2019Causality in Application and Lecture ExampleCausality_in_Application
16.04.2019Introduction to Causal Graphical ModelsIntroduction_to_Causal_Graphical_Models
17.04.2019Introduction to Conditional Independence Testing (I/II)Introduction_to_Conditional_Independence_Testing
23.04.2019Introduction to Conditional Independence Testing (II/II) 
24.04.2019Introduction to Constraint-Based Causal Structure LearningIntroduction_to_Constraint-Based_Causal_Structure_Learning
30.04.2019Topic Discussions and Individual Meetings 
07.05. - 21.05.2019Individual Meetings 
28.05.2019Intermediate Presentation 
29.05.2019Individual Presentation Recap and Next Steps 
04.06.2019Introduction to Causal Calculus (I/II)Introduction_to_Causal_Calculus
05.06.2019Introduction to Causal Calculus (II/II) 
11.06. - 02.07.2019Individual Meetings 
03.07.2019Final Presentation 
16.07.2019Scientific WritingScientific Writing
05.08.2019Draft Submission 
16.08.2019Review Submission  
30.08.2019Final Submission