Anthony StrittmatterView profile
Professor
Anthony Strittmatter is a Professor of Applied Econometrics at UniDistance Suisse in Brig/Valais, Switzerland. He maintains significant research affiliations with the Center for Research in Economics and Statistics (CREST), the University of Johannesburg, the CESifo Network, the Institut des Politiques Publiques (IPP), and the Swiss Institute for Empirical Economic Research (SEW-HSG). His academic career spans multiple prestigious institutions across Europe and focuses on the intersection of econometrics, machine learning, and economic policy analysis. Strittmatter received his Diploma in Economics from Albert-Ludwigs University Freiburg in 2009, following studies at Albert-Ludwigs University Freiburg, University Karlsruhe (TH), and Corvinus University Budapest. He continued his academic journey at the University of St. Gallen (HSG), where he earned his Ph.D. in Economics and Finance in 2013 under the supervision of Prof. Bernd Fitzenberger and Prof. Dr. Michael Lechner. His research focuses on the innovative integration of causal machine learning with traditional econometric methods to address complex policy evaluation questions. Strittmatter specializes in analyzing heterogeneous treatment effects, with particular applications in labor economics, business economics, and health economics. His methodological expertise in data analytics allows him to tackle challenging questions regarding optimal policy design and implementation using both experimental and non-experimental data. Analysis of Strittmatter's publication record reveals a consistent trajectory of methodological innovation applied to substantive economic questions. His work demonstrates a growing emphasis on causal machine learning techniques, particularly for estimating heterogeneous treatment effects. There's a clear pattern of applying these advanced methods to pressing policy issues, especially in labor market interventions, gender economics, and public policy evaluation. His recent work increasingly leverages big data approaches to address longstanding questions in economics with greater precision. While specific awards are not prominently listed in the available information, Strittmatter's extensive collaboration with leading researchers and his affiliation with prestigious research networks indicate significant recognition within the economics community. His work has been published in top-tier economics journals including the American Economic Review. Strittmatter actively contributes to the academic community through research collaborations and knowledge dissemination. His GitHub presence shows commitment to making research methods accessible through well-documented code repositories. His teaching focuses on causal machine learning, as evidenced by his CML-Course materials which provide practical coding sessions for economic applications. He leads and participates in research teams focused on causal inference methodology and its applications. His work with Michael Lechner, Uwe Sunde, and other collaborators demonstrates a strong commitment to advancing the methodological frontier in econometrics while maintaining relevance to real-world policy questions.
