Prof. Dr. Jannis Kück

Professor of Economics, in particular Data Science in Economics

Düsseldorf Institute for Competition Economics (DICE)
Heinrich-Heine-Universität Düsseldorf
Universitätsstr. 1
40225 Düsseldorf Building: 24.31
Floor/room: 01.07

+49 211 81-10238
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2022	Visiting Scholar at University of Fribourg, Switzerland (Chair of Applied Econometrics), Prof. Martin Huber
2020	Ph. D. in Statistics, Faculty of Business Administration (University of Hamburg), Prof. Martin Spindler
2019	Visiting Scholar at University of California, Irvine (Department of Economics/Deep Data Lab), Prof. Matthew Harding
2016	M.Sc. Business Mathematics, University of Hamburg
2014	B.Sc. Business Mathematics, University of Hamburg

Since 2023	Professor of Economics, esp. Data Science in Economics, Düsseldorf Institute for Competition Economics (DICE), Heinrich Heine University Düsseldorf
2021 - 2023	Post-Doctoral Research Associate at University of Hamburg, Faculty of Business Administration, Institute of Statistics - Research in Causal Machine Learning and Econometrics

Luo, Y., Spindler, M. & Kück, J. (2025). High-Dimensional L2-Boosting: Rate of Convergence. Journal of Machine Learning Research, 26(89), 1-54.
Bach, P., Klaassen, S., Kueck, J., & Spindler, M. (2025). Estimation and uniform inference in sparse high-dimensional additive models. Journal of Econometrics, 249(B), 105973.
Chernozhukov, V., Klaaßen, S., Kueck J., Spindler, M. (2022): Uniform Inference in High-Dimensional Gaussian Graphical Models. (Biometrika, available here)
Kueck, J., Luo, Y., Spindler, M., Wang, Z. (2022): Estimation and Inference of Treatment Effects with L2-Boosting in High-Dimensional Settings. (Journal of Econometrics, available here)
Felderer, B., Kueck, J., Spindler, M. (2022): Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-mode Online Panel (Social Science Computer Review, available here).
Klaaßen, S., Kueck, J., Spindler, M. (2021): Transformation Models in High Dimensions. (Journal of Business & Economic Statistics, available here)
Kueck, J. (2020): Advances in Machine Learning: Valid Inference about High-Dimensional Parameters. (available here) Dissertation, Staats-und Universitätsbibliothek Hamburg Carl von Ossietzky.

Huber, M., Kueck, J. (2022): Testing the Identification of Causal Effects in Observational Data . Under Review.
Apfel, N., Hatamyar, J., Huber, M., Kueck, J. (2024): Learning control variables and instruments for causal analysis in observational data. Under Review.
Klaassen, S., Kueck, J., Spindler, M. (2024): A Note on High-Dimensional Confidence Regions with Application to Linear Regression. Reject & Resubmit at Annals of Statistics.
Gomes, L., Kueck, J., Mattes, M., Spindler, M., Zaytsev, A. (2024): Collusion Detection with Graph Neural Networks. Under Review.
Huber, M., Kueck, J., Mattes, M. (2025): Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder. Under Review.
Bach, P., Klaassen, S., Kueck, J., Mattes, M., Spindler, M. (2025): Sensitivity Analysis for Evaluation of Natural Experiments with Difference-in-Differences. Under Review.
Czarnowske, D., Kueck, J., Stammann, A. (2025): Debiased Lasso Inference for High-Dimensional Panel Models with Fixed Effects. Under Review.
Klaassen, S., Rabenseifer, J., Kueck, J., Bach, P. (2025): Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators. Under Review.

High dimensional statistics
Econometrics
Causal Inference
Machine Learning
Deep Learning
Graphical Models

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