Dr. Sven Klaaßen

Research Interests

• Inference in High-Dimensional Settings
• Machine Learning
• Causal Inference
• Deep Learning
• Empirical Processes

Curriculum Vitae (available here)

Publications

Farbmacher, H., Guber, R. and Klaassen, S., 2020. Instrument Validity Tests with Causal Forests. Journal of Business & Economic Statistics, pp.1-10. (available here)

Klaassen, S., Kück, J. and Spindler, M., 2021. Transformation models in high-dimensions. Journal of Business & Economic Statistics, (just-accepted), pp.1-30. (available here) 

Klaassen, S., 2020. Essays on Valid Inference in High-dimensions, Dissertation

Working Papers

Klaassen, S., Kück, J., Spindler, M. and Chernozhukov, V., 2018. Uniform inference in high-dimensional gaussian graphical models. (Revised & Resubmit at Biometrika, available here)

Bach, P., Klaassen, S., Kueck, J. and Spindler, M., 2020. Uniform Inference in High-Dimensional Generalized Additive Models. (available here)

Klaassen, S., 2021. A Note on High-Dimensional Confidence Regions. (available here)

Work in Progress

Quantile Boosting

Average Conditional Quantile Treatment Effects

Adaptive diskrete Smoothing (with Ye Luo and Martin Spindler)

Controlling the False Discovery Rate for Heterogenous Treatment Effects

Education