Dr. Philipp Bach

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