(see also
Google
scholar and
ORCID)
Preprints:
- D. Hangartner, M. Marbach, L. Henckel, M.H. Maathuis, R.R. Kelz and
L. Keele. Profiling compliers in instrumental variables designs.
(arXiv:2103.06328)
- D. Deuber, J. Li, S. Engelke and M.H.
Maathuis. Estimation and inference of extremal quantile treatment effects
for heavy-tailed distributions. (arXiv:2110.06627)
- J. Scire, J.S. Huisman, D.C. Angst, J. Li, M.H. Maathuis, S.
Bonhoeffer, T. Stadler. estimateR: An R package to estimate and
monitor the effective reproductive number. (medRxiv:2022.06.30.22277095)
-
L. Henckel, M. Buttenschön and M.H. Maathuis. Graphical tools for
selecting accurate and valid conditional instrumental sets. (arXiv:2208.03697)
- J. Li, M.H. Maathuis and J.J. Goeman. Simultaneous false discovery
proportion bounds via knockoffs and
closed testing. (arXiv:2212.12822)
Peer-reviewed publications:
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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).
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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).
- 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)
- 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)
- 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)
- 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)
- D. Colombo and M.H. Maathuis (2014). Order-independent
constraint-based causal structure learning. Journal of Machine
Learning
Research 15 3741-3782. (published version)
- 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)
- 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)
- 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).
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
-
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:
- M.H. Maathuis (2006).
Nonparametric estimation for current status data with competing
risks.
Ph.D. thesis, University of Washington.
- M.H. Maathuis (2003).
Nonparametric maximum likelihood estimation
for bivariate censored data.
Master's thesis, Delft University of Technology, The Netherlands.