Marloes Maathuis

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(see also Google scholar and ORCID)

Preprints:

Peer-reviewed publications:

  1. J.S. Huisman, J. Scire, D.C. Angst, J. Li, R.A. Neher, M.H. Maathuis, S. Bonhoeffer, T. Stadler (2022). Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. eLife 11:e71345. (published version)
  2. S. Hiltbrunner, M.-L. Spohn, R. Wechsler, D. Akhoundova, L. Bankel, S. Kasser, S. Bihr, C. Britschgi, M.H. Maathuis, A. Curioni-Fontecedro (2022). Comprehensive statistical exploration of prognostic (bio-)markers for responses to immune checkpoint inhibitor in patients with non-small cell lung cancer. Cancers 14 75. (published version)
  3. L. Henckel, E. Perković and M.H. Maathuis (2022). Graphical criteria for efficient total effect estimation via adjustment in causal linear models. Journal of the Royal Statistical Society: Series B 84 579–599. (arXiv:1907.02435, published version)
  4. J. Li and M.H. Maathuis (2021). GGM knockoff filter: False discovery rate control for Gaussian graphical models. Journal of the Royal Statistical Society: Series B 83 534–558. (arXiv:1908.11611, published version)
  5. S.K. Mettler, J. Park, O. Özbek, L.K. Mettler, P.H. Ho, H.C. Rhim, M.H. Maathuis (2021). The importance of timely contact tracing — A simulation study. International Journal of Infectious Diseases 108 309-319. (published version)
  6. J. Witte, L. Henckel, M.H. Maathuis and V. Didelez (2020). On efficient adjustment in causal graphs. Journal of Machine Learning Research 21 1-45. (published version)
  7. N. Anderegg, J. Hector, L.F. Jefferys, J. Burgos-Soto, M.A. Hobbins, J. Ehmer, L. Meier, M.H. Maathuis, M. Egger (2020). Loss to follow-up correction increased mortality estimates in HIV-positive people on antiretroviral therapy in Mozambique. Journal of Clinical Epidemiology 128 83-92. (published version, medRxiv 2020.08.04.20167155v1)
  8. S.K. Mettler, J. Kim and M.H. Maathuis (2020). Diagnostic serial interval as a novel indicator for contact tracing effectiveness exemplified with the SARS-CoV-2/COVID-19 outbreak in South Korea. International Journal of Infectious Diseases 99 346-351. (published version, medRxiv 2020.05.05.20070946)
  9. M. Eigenmann, S. Mukherjee and M.H. Maathuis (2020). Evaluation of Causal Structure Learning Algorithms via Risk Estimation. Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020). In J. Peters and D. Sontag (Eds.), Proceedings of Machine Learning Research 124 151-160. (published version)
  10. Z. Baranczuk, J. Estill, S. Blough, S. Meier, A. Merzouki, M.H. Maathuis, O. Keiser (2019). Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries. Journal of the International AIDS Society, 22:e25437. (doi:10.1002/jia2.25437)
  11. B. Frot, P. Nandy and M.H. Maathuis (2019). Robust causal structure learning with some hidden variables. Journal of the Royal Statistical Society: Series B, 81, 459–487. (doi:10.1111/rssb.12315, arXiv:1708.01151)
  12. P. Nandy, A. Hauser and M.H. Maathuis (2018). High-dimensional consistency in score-based and hybrid structure learning. Annals of Statistics 46 3151-3183. (doi:10.1214/17-AOS1654, arXiv:1507.02608)
  13. H. Fröhlich, R. Balling, N. Beerenwinkel, O. Kohlbacher, S. Kumar, T. Lengauer, M.H. Maathuis, Y. Moreau, S.A. Murphy, T.M. Przytycka, M. Rebhan, H. Röst, A. Schuppert, M. Schwab, R. Spang, D. Stekhoven, J. Sun, A. Weber, D. Ziemek, B. Zupan (2018). From hype to reality: data science enabling personalized medicine. BMC Medicine 16: 150. (doi:10.1186/s12916-018-1122-7).
  14. E. Perković, J. Textor, M. Kalisch and M.H. Maathuis (2018). Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs. Journal of Machine Learning Research 18 (220): 1-62. (published version)
  15. C. Heinze-Deml, M.H. Maathuis and N. Meinshausen (2018). Causal structure learning. Annual Review of Statistics and Its Application 5 371-391. (doi:10.1146/annurev-statistics-031017-100630, arXiv:1706.09141)
  16. C. Nowzohour, M.H. Maathuis and P. Bühlmann (2017). Distributional equivalence and structure learning for bow-free acyclic path diagrams. Electronic Journal of Statistics 11 5342-5374. (doi:10.1214/17-EJS1372, code on github)
  17. E. Perković, M. Kalisch and M.H. Maathuis (2017). Interpreting and using CPDAGs with background knowledge. In G. Elidan and K. Kersting (Eds.), Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI-17). (published version, supplement)
  18. M. Eigenmann, P. Nandy and M.H. Maathuis (2017). Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges. In G. Elidan and K. Kersting (Eds.), Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI-17). (published version, supplement)
  19. P. Nandy, M.H. Maathuis and T.S. Richardson (2017). Estimating the effect of joint interventions from observational data in sparse high-dimensional settings. Annals of Statistics 45 647-674. (doi:10.1214/16-AOS1462)
  20. M. Drton and M.H. Maathuis (2017). Structure learning in graphical modeling. Annual Review of Statistics and Its Application 4 365-393 (doi:10.1146/annurev-statistics-060116-053803 , arXiv:1606.02359)
  21. A. Aigner, A. Curt, L.G. Tanadini and M.H. Maathuis (2017). Concurrent validity of single and groups of walking assessments following acute spinal cord unjury. Spinal Cord 55 435–440. (doi:10.1038/sc.2016.148).
  22. P. Kassraian Fard, C. Matthis, J.H. Balsters, M.H. Maathuis, N. Wenderoth (2016). Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example. Frontiers in Psychiatry 7:177. (doi: 10.3389/fpsyt.2016.00177)
  23. M.H. Maathuis and P. Nandy (2016). A review of some recent advances in causal inference. In P. Bühlmann, P. Drineas, M. Kane and M.J. van der Laan (Eds.), Handbook of Big Data, pp 387-407. Chapman and Hall/CRC, Boca Raton, FL. (arXiv:1506.07669)
  24. E. Perković, J. Textor, M. Kalisch and M.H. Maathuis (2015). A complete generalized adjustment criterion. In M. Meila and T. Heskes (Eds.), Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI-15), pp 682-691. AUAI Press, Corvallis, OR. 2015. (published version)
  25. M.H. Maathuis and D. Colombo (2015). A generalized back-door criterion. Annals of Statistics 43 1060-1088. (doi 10.1214/14-AOS1295, arXiv:1307.5636)
  26. D. Colombo and M.H. Maathuis (2014). Order-independent constraint-based causal structure learning. Journal of Machine Learning Research 15 3741-3782. (published version)
  27. A. Sokol, M.H. Maathuis and B. Falkeborg (2014). Quantifying identifiability in independent component analysis. Electronic Journal of Statistics 8 1438-1459. (doi 10.1214/14-EJS932)
  28. S. van de Geer and M.H. Maathuis (2013). Discussion of the paper by Piet Groeneboom: Nonparametric (smoothed) likelihood and integral equations. Journal of Statistical Planning and Inference 143 2068-2071. (doi:10.1016/j.jspi.2013.07.002)
  29. S. Ravizza, J.A.D. Atkin, M.H. Maathuis and E.K. Burke (2013). A combined statistical approach and ground movement model for improving taxi time estimations at airports. Journal of the Operational Research Society 64 1347-1360. (doi 10.1057/jors.2012.123).
  30. D.J. Stekhoven, L. Hennig, G. Sveinbjörnsson, I. Moraes, M.H. Maathuis, P. Bühlmann (2012). Causal stability ranking. Bioinformatics 28 2819-2823. (doi: 10.1093/bioinformatics/bts523)
  31. M. Kalisch, M. Mächler, D. Colombo, M.H. Maathuis and P. Bühlmann (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software, Vol 47, Issue 11, 1-26. (doi:10.18637/jss.v047.i11)
  32. D. Colombo, M.H. Maathuis, M. Kalisch and T.S. Richardson (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables. Annals of Statistics 40 294-321. (doi:10.1214/11-AOS940)
  33. T. Gsponer, M. Petersen, M. Egger, S. Phiri, M.H. Maathuis, A. Boulle, H. Tweyad, K. Peter, B.H. Chi and O. Keiser (2012). The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring. AIDS 26, 57-65. (doi: 10.1097/QAD.0b013e32834e1b5f)
  34. N. Meinshausen, M.H. Maathuis and P. Bühlmann (2011). Asymptotic optimality of the Westfall-Young permutation procedure for multiple testing under dependence. Annals of Statistics 39, 3369-3391. (doi:10.1214/11-AOS946)
  35. M.H. Maathuis and M.G. Hudgens (2011). Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times. Biometrika 98, 325-340. (doi:10.1093/biomet/asq083, arXiv:0909.4856)
  36. P. Bühlmann, M. Kalisch and M.H. Maathuis (2010). Variable selection in high-dimensional models: partially faithful distributions and the PC-simple algorithm. Biometrika 97, 261-278. (doi:10.1093/biomet/asq008, arXiv:0906.3204)
  37. M.H. Maathuis, D. Colombo, M. Kalisch and P. Bühlmann (2010). Predicting causal effects in large-scale systems from observational data. Nature Methods 7, 247-248. (doi:10.1038/nmeth0410-247, supplement) (See also the editorial on cause and effect in the same issue)
  38. M. Kalisch, B. Fellinghauer, E. Grill, M.H. Maathuis, U. Mansmann, P. Bühlmann and G. Stucki (2010). Understanding human functioning using graphical models. BMC Medical Research Methodology 10:14. (doi:10.1186/1471-2288-10-14)
  39. A.J. Caesar, T. Caesar and M.H. Maathuis (2010). Pathogenicity, characterization and comparative virulence of Rhizoctonia spp. from insect-galled roots of Lepidium draba in Europe. Biological Control 52, 140-144. (doi:10.1016/j.biocontrol.2009.10.006)
  40. M.H. Maathuis, M. Kalisch, P. Bühlmann (2009). Estimating high-dimensional intervention effects from observational data. Annals of Statistics 37, 3133-3164. (doi:10.1214/09-aos685)
  41. P. Groeneboom, M.H. Maathuis and J.A. Wellner (2008). Current status data with competing risks: limiting distribution of the MLE. Annals of Statistics 36, 1064-1089. (doi:10.1214/009053607000000983)
  42. P. Groeneboom, M.H. Maathuis and J.A. Wellner (2008). Current status data with competing risks: consistency and rates of convergence of the MLE. Annals of Statistics 36, 1031-1063. (doi:10.1214/009053607000000974)
  43. M.H. Maathuis and J.A. Wellner (2008). Inconsistency of the MLE for the joint distribution of interval censored survival times and continuous marks. Scandinavian Journal of Statistics 35, 83-103. (doi:10.1111/j.1467-9469.2007.00568.x, arXiv:math/0509084v2)
  44. M.G. Hudgens, M.H. Maathuis and P.G. Gilbert (2007). Nonparametric estimation of the joint distribution of a survival time subject to interval censoring and a continuous mark. Biometrics 63, 372-380. (doi:10.1111/j.1541-0420.2006.00709.x, supplement)
  45. M.H. Maathuis (2005). Reduction algorithm for the NPMLE for the distribution function of bivariate interval censored data. Journal of Computational and Graphical Statistics 14, 352-362. (doi:10.1198/106186005X48470, arXiv:0906.3215v1) (Winner of the 2004 student paper award of the ASA Statistical Computing and Graphics Sections)

Edited books/proceedings:

  1. C. de Campos and M.H. Maathuis (2021). Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, volume 161.
  2. U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C Robardet (Eds) (2020). Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings. Springer, Cham, Switzerland. Part III: LNAI 11908. See also https://www.ecmlpkdd2019.org.
  3. U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C Robardet (Eds) (2020). Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings. Springer, Cham, Switzerland. Part II: LNAI 11907. See also https://www.ecmlpkdd2019.org.
  4. U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C Robardet (Eds) (2020). Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings. Springer, Cham, Switzerland. Part I: LNAI 11906. See also https://www.ecmlpkdd2019.org.
  5. M. Maathuis, M. Drton, S. Lauritzen and M. Wainwright (Eds) (2019). Handbook of Graphical Models. Chapman&Hall/CRC Handbooks of Modern Statistical Methods. CRC Press, Boca Raton, FL. ISBN: 978-1-4987-8862-5.
  6. F. Eberhardt, E. Bareinboim, M.H. Maathuis, J. Mooij and R. Silva (Eds) (2016). Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application. CEUR Workshop Proceedings Vol-1792.
  7. M. Banerjee, F. Bunea, J. Huang, V. Koltchinskii and M.H. Maathuis (Eds) (2013). From Probability to Statistics and Back: High-Dimensional Models and Processes - A Festschrift in Honor of Jon A. Wellner. IMS Collections, Volume 9. ISBN: 978-0-940600-83-6.

Theses:

  1. M.H. Maathuis (2006). Nonparametric estimation for current status data with competing risks. Ph.D. thesis, University of Washington.
  2. M.H. Maathuis (2003). Nonparametric maximum likelihood estimation for bivariate censored data. Master's thesis, Delft University of Technology, The Netherlands.