Marloes Maathuis

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(see also Google scholar and ORCID)

Preprints:

Edited books/proceedings:

  1. 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.
  2. 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.

Publications:

  1. C. Heinze-Deml, M.H. Maathuis and N. Meinshausen (2018). Causal structure learning. Annual Review of Statistics and Its Applications, to appear. (arXiv:1706.09141, doi: 10.1146/annurev-statistics-031017-100630)
  2. 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), to appear. (arXiv:1707.02171)
  3. 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), to appear. (arXiv:1707.07560)
  4. 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. (abstract, published version)
  5. M. Drton and M.H. Maathuis (2017). Structure learning in graphical modeling. Annual Review of Statistics and Its Applications 4 365-393 (doi:10.1146/annurev-statistics-060116-053803 , arXiv:1606.02359)
  6. 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)
  7. A. Aigner, A. Curt, L.G. Tanadini and M.H. Maathuis (2016). Concurrent validity of single and groups of walking assessments following acute spinal cord unjury. Spinal Cord. (doi:10.1038/sc.2016.148).
  8. 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)
  9. E. Perković, J. Textor, M. Kalisch and M.H. Maathuis (2015). A complete 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)
  10. 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)
  11. D. Colombo and M.H. Maathuis (2014). Order-independent constraint-based causal structure learning. Journal of Machine Learning Research 15 3741-3782. (published version)
  12. 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, arXiv:1401.7899v1)
  13. 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)
  14. 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).
  15. 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)
  16. 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)
  17. 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, arXiv:1104.5617v2)
  18. 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)
  19. 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, arXiv:1106.2068v2)
  20. 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. (arxiv:0909.4856v3, published version)
  21. 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. (arXiv:0906.3204v3, published version)
  22. 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. (published version, supplementary information) (See also the editorial on cause and effect in the same issue)
  23. 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. (open source published version)
  24. 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. (preprint, published version)
  25. 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, arXiv:0810.4214v3)
  26. 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, arXiv:math/0609021v2)
  27. 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, arXiv:math/0609020v2)
  28. 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)
  29. 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, supplementary information)
  30. 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)

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.