I am a PhD candidate at HelmholtzAI in Munich, under the supervision of Niki Kilbertus.

My research is centered on statistical challenges and methodological problems at the intersection of causal inference and machine learning. My work addresses challenges in instrumental variable settings with applications in experimental design settings and biological data. Current projects include the circular question how experimental design can enable causal explanations and how we can let causal explanations drive the experimental design.

My motivation towards causal machine learning stems from my industry experience. Everybody is looking for explanations and reasons behind different kind of outcomes, sometimes without taking notice how much of the so-called explanations are based on pure correlation. I am curious how we can gain better insights from our data that we might actually call causal at some point.

Interests:
Causality, Interpretable Machine Learning, Experimental Design, Causality in Time Series

Education: