PhD Courses
The HCHE regularly offers courses for doctoral students in order to support young scientists. The courses usually last a few days to a week and are held by renowned, mostly international scientists.
In 2021 the following courses took place:
Supervised and Causal Machine Learning
Participants in this course learn common concepts and methods of Causal Machine Learning to analyze the effects of experimental or observational policy interventions. Causal Machine Learning combines two mature fields of data analytics. On the one hand, the field of machine learning (ML) has improved our ability to identify correlation patterns in data, which is important for making high-quality predictions. On the other hand, the field of causal inference has increased our knowledge of how to evaluate the impact of interventions, which is essential for high-quality decision making. The promise of causal machine learning is to deliver the best of both worlds, to draw (more reliable) and more informative causal inference.
oTree
This workshop is about developing experiments with oTree and is intended for people who have little or no experience with oTree. After the workshop, you will know everything you need to comfortably start developing and deploying your own experiments with oTree.