Research

Publications

Bach, P., Chernozhukov, V., Klaassen, S., Kurz, M., Spindler, M. (2024): DoubleML – An ObjectOriented Implementation of Double Machine Learning in R (https://doi.org/10.18637/jss.v108.i03). Journal of Statistical Software, 108 (3), 1-56
Schacht, O., Klaassen, S., Schwarz, P., Spindler, M., Grünbaum, D., Imhof, S., (2023): Causally Learning an Optimal Rework Policy (https://proceedings.mlr.press/v218/schacht23a.html). Proceedings of Machine Learning Research 218, 3-24.
Klaassen, S., Kueck, J., Spindler, M., Chernozhukov, V. (2023): Uniform Inference in HighDimensional Gaussian Graphical Models (https://doi.org/10.1093/biomet/asac030). Biometrika 110 (1), 51-68.
Klaassen, S., Kueck, J., Spindler, M. (2022): Transformation Models in High-Dimensions (https://www.tandfonline.com/doi/full/10.1080/07350015.2021.1906259). Journal of Business & Economic Statistics 40 (3), 1168-1178.
Farbmacher, H., Guber, R., Klaassen, S. (2022): Instrument Validity Tests with Causal Forests (https://www.tandfonline.com/doi/full/10.1080/07350015.2020.1847122). Journal of Business & Economic Statistics 40 (2), 605-614.

Bach, P., Klaassen, S., Kueck, J., Spindler, M. (2023): Uniform Inference in High-Dimensional Additive Models (https://doi.org/10.48550/arXiv.2004.01623). Reject and Resubmit at Journal of Econometrics.
Klaassen, S. (2021): A Note on High-Dimensional Confidence Regions (https://doi.org/10.48550/arXiv.2105.09028).
Klaassen, S., Teichert-Kluge, J., Bach, P., Chernozhukov, V., Spindler, M., Vijaykumar, S. (2024): DoubleMLDeep: Estimation of Causal Effects with Multimodal Data (https://doi.org/10.48550/arXiv.2402.01785). Submitted to International Conference of Machine Learning.
Bach, P., Schacht, O., Chernozhukov, V., Klaassen, S., Spindler, M. (2024): Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study (https://doi.org/10.48550/arXiv.2402.04674). Accepted at Causal Learning and Reasoning, forthcoming in Proceedings of Machine Learning Research.