Prof. Dr. Jannis Kück

Professor für Volkswirtschaftslehre, insb. Data Science in Economics

Düsseldorf Institute for Competition Economics (DICE)
Heinrich-Heine-Universität Düsseldorf
Universitätsstr. 1
40225 Düsseldorf Gebäude: 24.31
Etage/Raum: 01.07

+49 211 81-10238
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2022	Forschungsaufenthalt an der Universität Freiburg, Schweiz (Lehrstuhl für Angewandte Ökonometrie), Prof. Martin Huber
2020	Promotion in Statistik unter der Betreuung von Herr Prof. Dr. Martin Spindler
2019	Forschungsaufenthalt an der University of California, Irvine (Department of Economics/Deep Data Lab), Prof. Matthew Harding
2016	M.Sc. Wirtschaftsmathematik, Universität Hamburg
2014	B.Sc. Wirtschaftsmathematik, Universität Hamburg

Seit 2023	Professor für Volkswirtschaftslehre, insb. Data Science in Economics, Düsseldorf Institute for Competition Economics (DICE), Heinrich-Heine-Universität Düsseldorf
2021 - 2023	Postdoc am Lehrstuhl für Statistik, Universität Hamburg

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 . In Prüfung.
Apfel, N., Hatamyar, J., Huber, M., Kueck, J. (2024): Learning control variables and instruments for causal analysis in observational data. In Prüfung.
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. In Prüfung.
Huber, M., Kueck, J., Mattes, M. (2025): Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder. In Prüfung.
Bach, P., Klaassen, S., Kueck, J., Mattes, M., Spindler, M. (2025): Sensitivity Analysis for Evaluation of Natural Experiments with Difference-in-Differences. In Prüfung.
Czarnowske, D., Kueck, J., Stammann, A. (2025): Debiased Lasso Inference for High-Dimensional Panel Models with Fixed Effects. In Prüfung.
Klaassen, S., Rabenseifer, J., Kueck, J., Bach, P. (2025): Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators. In Prüfung.

Hochdimensionale Statistik
Ökonometrie
Kausale Inferenz
Maschinelles Lernen
Deep Learning
Graphische Modelle

Verantwortlichkeit: DICE