Publications:
(most of these papers are on
Google
scholar)
- D.M. Weber, M. Seiler, U. Subotic, M. Kalisch, R. Weil (2019). Buddy
taping versus splint immobilization for paediatric finger fractures: a
randomized controlled trial. Journal of Hand Surgery (European Volume) 44
(6), 640-647. (published version)
- HR Bussell, CA Aufdenblatten, U Subotic, M Kalisch, G Staubli,
D.M. Weber, S. Tharakan (2019). Compartment pressures in children with
normal and fractured lower extremities. European Journal of Trauma and
Emergency Surgery 45 (3), 493-497. (published version)
- 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)
- Renaux, C., Buzdugan, L., Kalisch, M. and Bühlmann,
P. (2018). Hierarchical inference for genome-wide association studies: a
view on methodology with software. To appear in Computational Statistics
(with
discussion). Preprint arXiv:1805.02988
- 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)
- P.S. Sulser, M. Kalisch, D.M. Weber (2016). Retroauricular
full-thickness skin grafts in syndactyly repair: outcome and comparison
with inguinal full-thickness skin grafts: retrospective (cross-sectional)
study. Journal of plastic surgery and hand surgery 50 (5), 281-285
(published version)
- L. Buzdugan, M. Kalisch, A. Navarro, D. Schunk, E. Fehr and
Bühlmann, P. (2016). Assessing statistical significance in
multivariable genome wide association analysis. Bioinformatics,
published online.
(published version)
- E. Perkovic, 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)
- S.J. Tharakan, U. Subotic, M. Kalisch, G. Staubli, D.M. Weber (2015).
Compartment Pressures in Children With Normal and Fractured Forearms: A
Preliminary Report. Journal of Pediatric
Orthopedics. (published version)
- M. Kalisch, P. Bühlmann (2014). Causal structure learning and
inference: a selective review. Quality Technology & Quantitative
Management 11, 3-21 (published version)
- P. Bühlmann, M. Kalisch, L. Meier (2014). High-dimensional
statistics with a view toward applications in biology. Annual Review of Statistics and its Applications
1, 255-278 (published version)
- P. Bühlmann, P. Rütimann and M. Kalisch (2013),
Controlling false positive selections in high-dimensional regression and
causal inference. Statistical Methods in Medical Research 22, 466-492. (pdf)
- D.M. Weber, M.A. Landolt, R. Gobet, M. Kalisch, N.K. Greeff
(2013). The Penile Perception Score: An Instrument Enabling
Evaluation by Surgeons and Patient Self-Assessment after
Hypospadias Repair. J. Urol., Vol 189, Issue 1,
189-193.(published version)
- M. Kalisch, M. Mächler, D. Colombo, M.H. Maathuis
and P.
Bühlmann (2012). Causal inference using graphical models with the R
package
pcalg. Journal for Statistical Software, Vol 47, Issue 11, 1-26.
(published version)
- 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.
(arXiv:1104.5617v2, published
version, extended abstract for UAI2011)
- F. Buller, M. Steiner, K. Frey, D. Mircsof, J. Scheuermann,
M. Kalisch, P. Bühlmann, C.T. Supuran, D. Neri (2011), "Selection of
carbonic anhydrase IX inhibitors from one million DNA-encoded
compounds", ACS Chem. Biol., 6 (4), 336-344.
(pdf)
- 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.
(pdf)
- 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. (pdf)
(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.
(pdf)
- M.H. Maathuis, M. Kalisch, P. Bühlmann (2009),
"Estimating high-dimensional intervention effects from observational
data",
Annals of Statistics 37, 3133-3164.
(pdf)
- C. Dahinden, M. Kalisch, P. Bühlmann (2009),
"Decomposition and model selection for large contingency tables",
Biometrical Journal 52:2, 233-252.
(pdf)
- D. Schöner, M. Kalisch, C. Leisner, L. Meier, M. Sohrmann,
M. Faty, Y. Barral, M. Peter, W. Gruissem, P. Bühlmann (2008),
"Annotating novel genes by integrating synthetic lethals and genomic
information",
BMC Systems Biology 2:3, 1-14.
(pdf)
- R.W. Lutz, M. Kalisch, P. Bühlmann (2008),
"Robustified L2 boosting",
Computational Statistics & Data Analysis
52, 3331-3341.
(pdf)
- M. Kalisch and P. Bühlmann (2008),
"Robustification of the PC-algorithm for directed acyclic graphs",
Journal of Computational and Graphical Statistics 17,
773-789.
(pdf)
- M. Kalisch, P. Bühlmann (2007),
"Estimating high-dimensional directed acyclic graphs with the PC-algorithm",
Journal of Machine Learning Research 8, 613-636.
(pdf)
Others:
- S. Lecomte, M. Kalisch, L. Krainer, G.J. Spuhler, R. Paschotta,
M. Golling, D. Ebling, T. Ohgoh, T. Hayakawa, S. Pawlik, B. Schmidt,
U. Keller (2005),
"Diode-pumped passively mode-locked Nd:YVO4 lasers with 40-GHz repetition
rate",
IEEE Journal of Quantum Electronics 41:1, 45-52.
(pdf)