Prof. Dr. h.c. Hasso Plattner

Causal Inference - Theory and Applications in Enterprise Computing

General Information

  • Teaching staff: Christopher HagedornJohannes HuegleDr. Michael Perscheid
  • 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Time: Tue and Wed 9.15 - 10.45
  • Room: whereby.com  (if not online;  D-E.9/10 )
  • First course: April 28, 2020
  • The course will be offered online (using whereby.com, a link is provided after enrolment). Thus, it is important that all participants enrol until April 22, 2020.
  • Individual group meetings may happen online, if circumstances not permitting in person meetings

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

The following is an outline of the course structure and may be subject to change.

28.04.2020Lecture Overview and Causal Inference in a NutshellCausal_Inference-Theory_and_Applications
29.04.2020Applications and ExercisesApplications_and_Jupyter-Lab
05.05.2020Introduction to Causal Graphical ModelsIntroduction_to_Causal_Graphical_Models
06.05.2020Group Topic Introduction & ExercisesGroup-Topics_and-CGM-Exercises
12.05.2020Introduction to Conditional Independence TestingIntroduction_to_Conditional_Independence-Tests
13.05.2020Exercise & Group Topic AssignmentGroup-Topics-Assignment_and_CI-Test-Exercises
19.05.2020Introduction to Constraint-Based Causal Structure LearningIntroduction_to_Causal_Structure_Learning
26.05.2020Introduction to Do Calculus of InterventionIntroduction_to_Causal_Calculus
02.06.2020Scientific WritingIntroduction_to_Scientific_Writing
03.06.2020Individual Group Meetings  
09.06.2020Intermediate Presentation 
10.06.2020Individual Group Meetings  
16.06 - 01.07.2020Elaboration Period 
08.07.2020Final Presentation 
14.07 - 05.08.2020Scientific Writing Period 
10.08.2020Draft Submission for Peer Review 
17.08.2020Review Submission  
31.08.2020Final Submission