The central task of machine learning research is to predict future observations by using data to automatically find dependencies in the world. Most machine learning methods build on statistics, which significantly limits their applicability. In fact, one can go beyond this and take a deep dive into causal structures underlying statistical dependences. Since causal models are more robust to changes in real-world data, we argue that causality can play a pivotal role in addressing some of the hard open problems of machine learning, such as explainability and generalisability. As such, the machine learning algorithms equipped with the ability of causal reasoning and learning can make better predictions, and will even contribute an indispensable part of Artificial General Intelligence (AGI).
We have opening positions for interns, postdocs, and full-time researchers. Welcome to contact me if you are interested in broad topics on causal machine learning.