Most papers are freely available (PDF links). Just ask me for the others.


  • Ewald, F. K., Bothmann, L., Wright, M. N., Bischl, B., Casalicchio, G. & König, G. (2024). A guide to feature importance methods for scientific inference. arXiv (accepted at xAI 2024). PDF
  • Koenen, N. & Wright, M. N. (2024). Toward understanding the disagreement problem in neural network feature attribution. arXiv (accepted at xAI 2024). PDF
  • Dandl, S., Blesch, K., Freiesleben, T., König, G., Kapar, J., Bischl, B. & Wright, M. N. (2024). CountARFactuals – Generating plausible model-agnostic counterfactual explanations with adversarial random forests. arXiv (accepted at xAI 2024). PDF
  • Langbein, S. H., Krzyziński, M., Spytek, M., Baniecki, H., Biecek, P. & Wright, M. N. (2024). Interpretable machine learning for survival analysis. arXiv. PDF
  • Koenen, N. & Wright, M. N. (2023). Interpreting deep neural networks with the package innsight. arXiv (accepted at Journal of Statistical Software). PDF
  • Dijkstra, L., Schink, T., Linder, R., Schwaninger, M., Pigeot, I., Wright, M. N., & Foraita, R. (2022). A discovery and verification approach for pharmacovigilance using electronic health care data. medRxiv. PDF
  • Blesch, K. & Wright, M. N. (2023). arfpy: A python package for density estimation and generative modeling with adversarial random forests. arXiv (accepted at Journal of Open Research Software). PDF

Journal Articles, Conference and Workshop Papers

  • Spytek, M., Krzyziński, M., Langbein, S. H., Baniecki, H., Wright, M. N. & Biecek, P. (2023). survex: an R package for explaining machine learning survival models. Bioinformatics 39. PDF
  • Molnar, C., Freiesleben, T., König, G., Herbinger, J., Reisinger, T., Casalicchio, G., Wright, M. N. & Bischl, B. (2023). Relating the partial dependence plot and permutation feature importance to the data generating process. World Conference on Explainable Artificial Intelligence (xAI) 2023. PDF
  • Blesch, K., Wright, M. N. & Watson, D. S. (2023). Unfooling SHAP and SAGE: Knockoff imputation for Shapley values. World Conference on Explainable Artificial Intelligence (xAI) 2023. PDF
  • Watson, D. S., Blesch, K., Kapar, J. & Wright, M. N. (2023). Adversarial random forests for density estimation and generative modeling. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS) PMLR 206:5357-5375. PDF
  • Hiabu, M., Meyer J. T. & Wright, M. N. (2023). Unifying local and global model explanations by functional decomposition of low dimensional structures. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS) PMLR 206:7040-7060. PDF
  • Blesch, K., Watson, D. S. & Wright, M. N. (2023). Conditional feature importance for mixed data. AStA Adv Stat Anal. PDF
  • Bonannella, C., Hengl, T., Heisig, J., Parente, L., Wright, M. N. Herold, M. & de Bruin, S. (2022). Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning. PeerJ 10:e13728. PDF
  • Mehlig, K., Foraita, R., Nagrani, R., Wright, M. N., De Henauw, S., Molnár, D., Moreno, L. A., Russo, P., Tornaritis, M., Veidebaum, T., Lissner, L., Kaprio, J. & Pigeot, I., on behalf of the IDEFICS and I.Family consortia (2023). Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children’s cohort. Diabetologia 66:1914–1924. PDF
  • Baudeu, R., Wright, M. N. & Loecher, M. (2022). Are SHAP values biased towards high-entropy features? ECML PKDD Workshop on eXplainable Knowledge Discovery in Data Mining. PDF
  • Watson, D. S. & Wright, M. N. (2021). Testing conditional independence in supervised learning algorithms. Machine Learning 110:2107-2129. PDF
  • Askland, K. D., Strong, D., Wright, M. N. & Moore, J. H. (2021). The translational machine: A novel machine-learning approach to illuminate complex genetic architectures. Genetic Epidemiology 45:485-536. PDF
  • Hüls, A. *, Wright, M. N. *, Bogl, L. H., Kaprio, J., Lissner, L., Molnár, D., Moreno, L., De Henauw, S., Siani, A., Veidebaum, T., Ahrens, W., Pigeot, I. & Foraita, R. (2021). Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents. International Journal of Obesity 45:1321-1330. PDF *Equal contribution
  • Wright, M. N., Kusumastuti S., Mortensen, L. H., Westendorp, R. G. J. & Gerds, T. A. (2021). Personalised need of care in an ageing society: The making of a prediction tool based on register data. Journal of the Royal Statistical Society: Series A (Statistics in Society) 184:1199-1219. PDF
  • Koenen, N., Wright, M. N., Maass, P. & Behrmann, J. (2021). Generalization of the change of variables formula with applications to residual flows. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models. PDF
  • Breau, B., Brandes, B., Wright, M. N., Buck, C., Vallis, L. A. & Brandes, M. (2020). Association of individual motor abilities and accelerometer-derived physical activity measures in preschool-aged children. Journal for the Measurement of Physical Behaviour 4:227-235. Free Postprint
  • Brandes, B., Buck, C., Wright, M. N., Pischke, C.R. & Brandes, M. (2020). Impact of “JolinchenKids—Fit and healthy in daycare” on children’s objectively measured physical activity: A cluster-controlled study. Journal of Physical Activity and Health 17:1025-1033. Free Postprint
  • Schmid, M., Welchowski T., Wright, M. N. & Berger, M. (2020). Discrete-time survival forests with Hellinger distance decision trees. Data Mining and Knowledge Discovery 34:812-832. PDF
  • Boulesteix, A-L., Wright, M. N., Hoffmann, S. & König, I. R. (2020). Statistical learning approaches in the genetic epidemiology of complex diseases. Human Genetics 139:73–84. Free read-only version
  • Weinhold, L., Schmid, M., Mitchell R., Maloney, K. O., Wright, M. N. & Berger, M. (2020). A random forest approach for modeling bounded outcome variables. Journal of Computational and Graphical Statistics 29:639-658. Free Preprint
  • Hornung, R. & Wright, M. N. (2019). Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. PDF
  • Steenbock, B., Wright, M. N., Wirsik, N. & Brandes, M. (2019). Accelerometry-based prediction of energy expenditure in preschoolers. Journal for the Measurement of Physical Behaviour 2:94-102. Free Preprint
  • Wright, M. N. & König, I. R. (2019). Splitting on categorical predictors in random forests. PeerJ 7:e6339. PDF
  • Probst, P., Wright, M. N. & Boulesteix, A-L. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery 9:e1301. Free Preprint
  • Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M. & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. PDF
  • Fouodo, C. J. K., König, I. R., Weihs, C., Ziegler A. & Wright, M. N. (2018). Support vector machines for survival analysis with R. The R Journal 10:412–423. PDF
  • Nembrini, S., König, I. R. & Wright, M. N. (2018). The revival of the Gini Importance? Bioinformatics 34:3711–3718. PDF
  • Hirose, M., Schilf, P., Gupta, Y., Zarse, K., Künstner, A., Fähnrich, A., Busch, H., Yin, J., Wright, M. N., Ziegler, A., Vallier, M., Belheouane, M., Baines, J. F., Tautz, D., Johann, K., Oelkrug, R., Mittag, J., Lehnert, H., Othman, A., Jöhren, O., Schwaninger, M., Prehn, C., Adamski, J., Shima, K., Rupp, J., Häsler, R., Fuellen, G., Köhling, R., Ristow, M. & Ibrahim, S. M. (2018). Low-level mitochondrial heteroplasmy modulates DNA replication, glucose metabolism and lifespan in mice. Scientific Reports 8:5872. PDF
  • Foraita, R., Dijkstra, L., Falkenberg, F., Garling, M., Linder, R., Pflock, R., Rizkallah, M. R., Schwaninger, M., Wright, M. N. & Pigeot, I. (2018). Detection of drug risks after approval: Methods development for the use of routine statutory health insurance data. Bundesgesundheitsblatt 61:1075–1081. PDF
  • Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software 77:1–17. PDF
  • Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruipérez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12:e0169748. PDF
  • Wright, M. N., Dankowski, T. & Ziegler, A. (2017). Unbiased split variable selection for random survival forests using maximally selected rank statistics. Statistics in Medicine 36:1272–1284. Free Preprint
  • Hirose, M., Schilf, P., Gupta, Y., Wright, M. N., Jöhren, O., Wagner, A. E., Sina, C., Ziegler, A., Ristow, M. & Ibrahim, S. M. (2016). Lifespan effects of mitochondrial mutations. Nature 540:E13–E14.
  • Schmid, M., Wright, M. N. & Ziegler, A. (2016). On the use of Harrell’s C for clinical risk prediction via random survival forests. Expert Systems with Applications 63:450–459. Free Preprint
  • Schirmer, J. H., Wright, M. N., Herrmann, K., Laudien, M., Nölle, B., Reinhold-Keller, E., Bremer, J. P., Moosig, F. & Holle, J. U. (2016). Myeloperoxidase-ANCA associated Granulomatosis with polyangiitis is a clinically distinct subset within ANCA-associated vasculitis. Arthritis & Rheumatology, 68:2953–2963. PDF
  • Wright, M. N., Ziegler, A. & König, I. R. (2016). Do little interactions get lost in dark random forests? BMC Bioinformatics 17:145. PDF
  • Schirmer, J. H., Wright, M. N., Vonthein, R., Herrmann, K., Nölle. B., Both, M., Henes, F., Arlt, A., Gross, W. L., Schinke, S., Reinhold-Keller, E., Moosig, F. & Holle, J. U. (2016). Clinical presentation and long-term outcome of 144 patients with microscopic polyangiitis in a monocentric German cohort. Rheumatology (Oxford) 55:71–79. PDF
  • Wright, M. N. & Ziegler, A. (2015). Multiple censored data in dentistry: A new statistical model for analyzing lesion size in randomized controlled trials. Biometrical Journal 57:384–394.
  • Paulick, C., Wright, M. N., Verleger, R. & Keller, K. (2014). Decomposition of 3-way arrays: A comparison of different PARAFAC algorithms. Chemometrics and Intelligent Laboratory Systems 137:97–109.

Book Chapters

  • Wright, M. N. (2023). Feature Selection. In: Bischl, B., Sonabend, R., Kotthoff, L., Lang, M., (Eds.) Applied Machine Learning Using mlr3 in R. CRC Press, Boca Raton, Florida. HTML
  • Dandl, S., Biecek, P., Casalicchio, G. & Wright, M. N. (2023). Model Interpretation. In: Bischl, B., Sonabend, R., Kotthoff, L., Lang, M., (Eds.) Applied Machine Learning Using mlr3 in R. CRC Press, Boca Raton, Florida. HTML
  • Binder, M., Pfisterer, F., Becker, M. & Wright, M. N. (2023). Non-sequential Pipelines and Tuning. In: Bischl, B., Sonabend, R., Kotthoff, L., Lang, M., (Eds.) Applied Machine Learning Using mlr3 in R. CRC Press, Boca Raton, Florida. HTML
  • Wright, M. N.*, Gola D.* & Ziegler A. (2017). Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform. In: Elston, R. C. (Ed.) Statistical Human Genetics (2nd edn.). Methods in Molecular Biology 1666:629-647. Humana Press, New York. HTML *Equal contribution



  • Pigeot, I., Fröhlich, H., Intemann, T., Prause, G. & Wright, M. N. (2023). KI und die Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten (NFDI4HEALTH). In Dössel, O., Schäffter, T., Rutert, B. (Hrsg.): Künstliche Intelligenz in der Medizin. Berlin-Brandenburgische Akademie der Wissenschaften 11:62-74. ISBN 978-3-949455-18-6. PDF