Heiler P. and Mareckova J. (2021), Shrinkage for Categorical Regressors, Journal of Econometrics, 223 (1). 161-189. ISSN 0304-4076
Mareckova J. (2019), Detecting Structural Breaks using a Fusion Lasso Penalty, University of Konstanz. https://kops.uni-konstanz.de/server/api/core/bitstreams/b6278612-8172-425e-ad03-4ed46336ca1c/content
Mareckova J. and Pohlmeier W. (2019), How well can Noncognitive Skills Predict Unemployment?: A Machine Learning Approach, University of Konstanz. https://www.researchgate.net/publication/332644086_How_well_can_Noncognitive_Skills_Predict_Unemployment_A_Machine_Learning_Approach
Lechner, M. and Mareckova, J. (2022). Modified Causal Forest. arXiv preprint arXiv:2209.03744. https://arxiv.org/abs/2209.03744
Lechner, M. and Mareckova, J. (2024). Comprehensive Causal Machine Learning. arXiv preprint arXiv:2405.10198. https://arxiv.org/abs/2405.10198
Mascolo, F., Bearth, N., Muny, F., Lechner, M. and Mareckova J. (2024). From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies. arXiv preprint arXiv:2410.23322. https://arxiv.org/abs/2410.23322
Hinge, Daniel: “Teaching machines to do monetary policy”, Central Banking, Published Oct 27, 2017. Partially based on an interview with me. https://www.centralbanking.com/technology/3270121/teaching-machines-to-do-monetary-policy
Nazarenko, Elena and Puttick, Alexandre: “Fairness and Bias in AI Applications for the Labor Market”, SocietyByte, Published Apr 23, 2024. Covering AMLD 2024 presentation. https://www.societybyte.swiss/en/2024/04/23/fairness-and-bias-in-ai-applications-for-the-labor-market/
2019-2024: Development of the R package “Modified Causal Forest” based on paper by Michael Lechner “Modified Causal Forests” jointly with M. Zimmert and S. Heiniger.