I am an Assistant Professor of “Data Science in Economics” at the School of Business and Economics of the University of Tübingen.
My research interests are at the intersection of causal inference and machine learning to answer questions in empirical, mostly labor, economics. In particular I am currently working on the estimation of average and heterogeneous treatment effects as well as policy learning. See my research section or Google Scholar for more details.
September 2022: Paper “Double Machine Learning based Program Evaluation under Unconfoundedness” published at The Econometrics Journal
September 2022: Update of working paper with Phillip Heiler called “Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments” available on arXiv
August 2022: I am excited to start my tenure track position at the University of Tübingen.
August 2022: Simulation paper with Michael Lechner and Anthony Strittmatter one of the Top Cited Papers of The Econometrics Journal, means free access until end of the year
October 2021: New working paper with Phillip Heiler called “Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments” available on arXiv
June 2021: Teaching PhD course in Supervised and Causal Machine Learning at the University of Hamburg
March 2021: Teaching PhD course in Causal Machine Learning at the GESIS Leibniz Institute for the Social Sciences
January 2021: Paper “Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence”, with Michael Lechner and Anthony Strittmatter published at The Econometrics Journal
January 2021: Single-authored paper “A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills” published at the Journal of the Royal Statistical Society: Series A