Dr. Philipp Bach
Wissenschaftlicher Mitarbeiter
Anschrift
Universität Hamburg
Fakultät für Betriebswirtschaft
Statistik mit Anwendung in der Betriebswirtschaftslehre
Moorweidenstr. 18
20148 Hamburg
Büro
Mo 18
Raum: 0009
Sprechzeiten
- nach Vereinbarung
Kontakt
Tel.: +49 40 42838-1538
E-Mail: philipp.bach"AT"uni-hamburg.de
Research Interests
- Estimation of causal effects and inference in high dimensionsal settings with machine learning methods
- Sensitivity analysis
- Difference-in-difference models
- Machine Learning and Deep Learning
- Applications in labor, energy, environmental, health and financial economics
Code
Code and supplemental materials of research projects and publications are available here.
Publications
- Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler (2024), Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study, Conference on Causal Learning and Reasoning, Proceedings of Machine Learning Research, 236, 1065-1117, available online.
- Philipp Bach, Victor Chernozhukov, Martin Spindler (2024). Heterogeneity in the U.S. Gender Wage Gap. Journal of the Royal Statistical Society: Series A, 187(1), 209-230, available online.
- Philipp Bach, Victor Chernozhukov, Malte S. Kurz, , Martin Spindler, Sven Klaassen (2024). DoubleML - An Object-Oriented Implementation of Double Machine Learning in R. DoubleML: An Object-Oriented Implementation of Double Machine Learning in R. Journal of Statistical Software, 108(3), 1–56, available online.
- Philipp Bach, Victor Chernozhukov, Malte S. Kurz, , Martin Spindler (2022).
DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python.
Journal of Machine Learning Research 23 (53), 1–6, available online. - Philipp Bach, Helmut Farbmacher, Martin Spindler, Semiparametric count data modeling with an application to health service demand, Econometrics and Statistics, 8, 125-140, 2018, available online.
Working Papers
- Sven Klaassen, Jan Teichert-Kluge, Philipp Bach, Victor Chernozhukov, Martin Spindler, Suhas Vijaykumar, DoubleMLDeep: Estimation of Causal Effects with Multimodal Data, available at arxiv, 2024.
- Philipp Bach, Sven Klaassen, Jannis Kueck, Martin Spindler, Estimation and Uniform Inference in Sparse High-Dimensional Additive Models, Working Paper, available at arXiv, 2020.
- Philipp Bach, Victor Chernozhukov, Martin Spindler, Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework, Working Paper, available at arxiv, 2020.
- Philipp Bach, Victor Chernozhukov, Martin Spindler, Closing the U.S. gender wage gap requires understanding its heterogeneity, Working Paper, available at arXiv, 2018.
- Philipp Bach, Victor Chernozhukov, Martin Spindler, Valid Simultaneous Inference in High-Dimensional Settings (with the HDM package for R), Working Paper, available at arXiv , 2018.
- Bach, Philipp, H. Farbmacher, and M. Spindler (2016): Semiparametric Count Data Modeling
with an Application to Health Service Demand. No. 16/20. HEDG, c/o Department of Economics,
University of York, 2016.
Education
Since 10/2021 |
Postdoc in Statistics, University of Hamburg, Chair of Statistics with Application in Business Administration |
2021 |
Ph.D., Hamburg University, Prof. Dr. Martin Spindler; Thesis: Applications in High-Dimensional Econometrics |
2016 | M.Sc. Economics, Ludwig-Maximilians-Universität München |
2013 | B.Sc. Economics, University of Mannheim |