Dominik's research centers around causal inference, heterogeneous data, high-dimensional statistics and graphical models. She is particularly interested in inference in settings where traditional statistical measures of uncertainty fail. For example, due to changing circumstances or confounding the distribution of the data might change between data sets. Developing better methods to deal with distribution shift could increase replicability of feature selections and reliability of prediction mechanisms.
- Causality, Spring 2022. Visiting Scientist.