M.Sc. Philipp Bach

Research Interests

• Estimation of causal effects and inference in high dimensionsal settings with machine learning methods
• Machine Learning and Deep Learning
• Applications in labor, health and financial economics

Code

Code and supplemental materials of research projects and publications are available here. 

Publications

• 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

• Philipp Bach, Sven Klaassen, Jannis Kueck, Martin Spindler, Uniform Inference in High-Dimensional Generalized Additive Models, 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