In this talk first an introduction to the double machine learning framework is given. This allows inference on parameters in high-dimensional settings. Then, two applications are given, namely transformation models and Gaussian graphical models in high-dimensional settings. Both kind of models are widely used by practitioners. As high-dimensional data sets become more and more available, it is important to allow situations where the number of parameters is large compared to the sample size.
- Chernozhukov, Hansen, Spindler, 2015: Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach. Annual Review of Economics.
- Chernozhukov, Klassen, Kück, Spindler, 2018: Gaussian Graphical Models in High-Dimensions. Work in Progress.
- Klassen, Kück, Spindler, 2017: Transformation Models in High-Dimensions, arxiv.