Publications:

(most of these papers are on Google scholar)

26. 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)
25. 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)
24. 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)
23. 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
22. 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)
21. 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)
20. 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)
19. 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)
18. 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)
17. M. Kalisch, P. Bühlmann (2014). Causal structure learning and inference: a selective review. Quality Technology & Quantitative Management 11, 3-21 (published version)
16. 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)
15. 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)
14. 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)
13. 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)
12. 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)
11. 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)
10. 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)
9. 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)
8. 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)
7. M.H. Maathuis, M. Kalisch, P. Bühlmann (2009), "Estimating high-dimensional intervention effects from observational data", Annals of Statistics 37, 3133-3164. (pdf)
6. C. Dahinden, M. Kalisch, P. Bühlmann (2009), "Decomposition and model selection for large contingency tables", Biometrical Journal 52:2, 233-252. (pdf)
5. 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)
4. R.W. Lutz, M. Kalisch, P. Bühlmann (2008), "Robustified L2 boosting", Computational Statistics & Data Analysis 52, 3331-3341. (pdf)
3. 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)
2. 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:

1. 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)