Eva-Maria Oeß
Research Focus
My research focuses on semiparametric methods for causal inference. I study how causal identifying assumptions can be tested empirically using panel data and how treatment-assignment rules can be learned from when both the effectiveness and cost of a treatment matter. Another area of my research is about understanding why Double Machine Learning and Targeted Maximum Likelihood Estimation can be different in finite samples.
Academic Career
- Since 2025: Fellow, Max Planck Institute for Behavioral Economics
- Since 2023: PhD student in Economics
- 2019–2023: MSc in Economics, University of Cologne
- 2015–2019: BA in Economics, University of Cologne
Publications
- Huber, M., and Oeß, E.-M. (2026). A Joint Test of Unconfoundedness and Common Trends. Journal of Applied Econometrics, 41(5), 684–709.