Beschreibung

This course introduces the foundations and modern practice of Causal Inference and its integration with Machine Learning. Participants learn to move beyond correlation, design quasi-experiments, analyze time series causality, and build interpretable, reliable models. Using Python tools and real-world cases, the course combines theory, hands-on coding, and applied decision-making.

Learning Goals:

  • Identifying and estimating causal effects
  • Designing and analyzing randomized and quasi-experimental studies
  • Applying ML-based causal methods for heterogeneous treatment effects
  • Understanding assumptions, identifiability, robustness, and limitations.
  • Building interpretable and reliable decision models for practice

What you get:

This course focuses on using Causal Inference and Machine Learning to understand cause–effect relationships in complex, data-driven systems. It provides a rigorous yet practical framework that combines statistical theory, modern ML methods, and real-world applications.

The course starts with causal foundations: potential outcomes, DAGs, confounding, and identification strategies. We then cover matching, weighting, instrumental variables, difference-in-differences, and regression discontinuity.

Next, we integrate ML approaches such as double machine learning, causal forests, meta-learners, and time-series causality (e.g., Granger causality and causal discovery). We also address explainable AI and applications in different domains.

Hands-on tutorials provide practical experience using R or Python and modern causal libraries for real-world datasets.

What you should bring:

This course is a blend of theoretical concepts and practical applications. Your willingness to engage with challenging material and a desire to explore the exciting intersection of AI and science will be your greatest assets.

Technical Skills

A brain that's not afraid of numbers. You don't need to be a math genius, but a basic understanding of things like calculus, linear algebra, and statistics will save you a lot of headaches. Just think of it as the secret code you need to speak.

R + Python Skills

You've got to know your way around R and Python and maybe a little PyTorch so you can boss the computer around. We highly recommend having comparable knowledge from the "Machine learning with Tensorflow"-course at hand.

A Sense of Humor:

When things get weird (and they will), a good laugh helps.

You should plan to spend around 10 hours per week working through the learning content and completing the assignments.

Additional time will be needed to prepare the final project presentation.

Allocating sufficient time is therefore critical.

To receive a certificate of achievement (Leistungszertifikat/ECTS) for this course, active participation is expected, no more than two classes may be missed, and you have to conduct an applied capstone project. At the end of the course, the project has to be presented and a well-documented project source code (GitHub) has to be submitted