Estimating high-dimensional directed acyclic graphs with the PC-algorithm. M Kalisch, P Bühlman Journal of Machine Learning Research 8 (3), 2007 | 806 | 2007 |
Causal inference using graphical models with the R package pcalg M Kalisch, M Mächler, D Colombo, MH Maathuis, P Bühlmann Journal of statistical software 47, 1-26, 2012 | 558 | 2012 |
Learning high-dimensional directed acyclic graphs with latent and selection variables D Colombo, MH Maathuis, M Kalisch, TS Richardson The Annals of Statistics, 294-321, 2012 | 339 | 2012 |
Estimating high-dimensional intervention effects from observational data MH Maathuis, M Kalisch, P Bühlmann The Annals of Statistics 37 (6A), 3133-3164, 2009 | 328 | 2009 |
Predicting causal effects in large-scale systems from observational data MH Maathuis, D Colombo, M Kalisch, P Bühlmann Nature methods 7 (4), 247-248, 2010 | 269 | 2010 |
High-dimensional statistics with a view toward applications in biology P Bühlmann, M Kalisch, L Meier Annual Review of Statistics and Its Application 1, 255-278, 2014 | 253 | 2014 |
Selection of carbonic anhydrase IX inhibitors from one million DNA-encoded compounds F Buller, M Steiner, K Frey, D Mircsof, J Scheuermann, M Kalisch, ... ACS chemical biology 6 (4), 336-344, 2011 | 129 | 2011 |
Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm P Bühlmann, M Kalisch, MH Maathuis Biometrika 97 (2), 261-278, 2010 | 103 | 2010 |
Robustification of the PC-algorithm for directed acyclic graphs M Kalisch, P Bühlmann Journal of Computational and Graphical Statistics 17 (4), 773-789, 2008 | 70 | 2008 |
The Penile Perception Score: an instrument enabling evaluation by surgeons and patient self-assessment after hypospadias repair DM Weber, MA Landolt, R Gobet, M Kalisch, NK Greeff The Journal of urology 189 (1), 189-193, 2013 | 67 | 2013 |
Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs E Perkovic, JC Textor, M Kalisch, MH Maathuis | 59 | 2018 |
Understanding human functioning using graphical models M Kalisch, BAG Fellinghauer, E Grill, MH Maathuis, U Mansmann, ... BMC Medical Research Methodology 10 (1), 1-10, 2010 | 55 | 2010 |
A complete generalized adjustment criterion E Perković, J Textor, M Kalisch, MH Maathuis arXiv preprint arXiv:1507.01524, 2015 | 53 | 2015 |
Causal structure learning and inference: a selective review M Kalisch, P Bühlmann Quality Technology & Quantitative Management 11 (1), 3-21, 2014 | 40 | 2014 |
Robustified L2 boosting RW Lutz, M Kalisch, P Bühlmann Computational Statistics & Data Analysis 52 (7), 3331-3341, 2008 | 38 | 2008 |
Assessing statistical significance in multivariable genome wide association analysis L Buzdugan, M Kalisch, A Navarro, D Schunk, E Fehr, P Bühlmann Bioinformatics 32 (13), 1990-2000, 2016 | 35 | 2016 |
Interpreting and using CPDAGs with background knowledge E Perković, M Kalisch, MH Maathuis arXiv preprint arXiv:1707.02171, 2017 | 34 | 2017 |
Diode-pumped passively mode-locked Nd: YVO/sub 4/lasers with 40-GHz repetition rate S Lecomte, M Kalisch, L Krainer, GJ Spuhler, R Paschotta, M Golling, ... IEEE journal of quantum electronics 41 (1), 45-52, 2005 | 26 | 2005 |
Decomposition and model selection for large contingency tables C Dahinden, M Kalisch, P Bühlmann Biometrical Journal: Journal of Mathematical Methods in Biosciences 52 (2 …, 2010 | 25 | 2010 |
pcalg: estimation of CPDAG/PAG and causal inference using the IDA algorithm M Kalisch, M Mächler, D Colombo URL http://CRAN. R-project. org/package= pcalg, 2010 | 17 | 2010 |