Causal Machine Learning

While AI and Machine Learning are mainly tailored for predictions, based on correlations, many important questions in industry and research are causal questions. Examples are pricing, marketing mix modelling, resource allocation, to name a few, or many questions in Corporate Finance, Human Resources or Management in general. The emerging field of Causal AI / ML combines causal inference with modern methods in machine learning as complex, to estimate causal effects in high-dimensional, complex data. Due to the rise of digitization, such data sets are more and more available and can be utilized for research. 

Goal: The participants will learn the fundamental concepts and methods of Causal Machine Learning, in particular the Double Machine Learning approach, and will be able to apply the methods in their empirical research.

Registration deadline: September 7, 2025