| Order-independent constraint-based causal structure learning. D Colombo, MH Maathuis J. Mach. Learn. Res. 15 (1), 3741-3782, 2014 | 952 | 2014 |
| 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 (11), 1-26, 2012 | 882 | 2012 |
| Learning high-dimensional directed acyclic graphs with latent and selection variables D Colombo, MH Maathuis, M Kalisch, TS Richardson Annals of Statistics 40, 294-321, 2012 | 664 | 2012 |
| From hype to reality: data science enabling personalized medicine H Fröhlich, R Balling, N Beerenwinkel, O Kohlbacher, S Kumar, ... BMC medicine 16 (1), 150, 2018 | 561 | 2018 |
| Estimating high-dimensional intervention effects from observational data MH Maathuis, M Kalisch, P Bühlmann | 505 | 2009 |
| Structure learning in graphical modeling M Drton, MH Maathuis Annual Review of Statistics and Its Applications 4, 365-393, 2017 | 363 | 2017 |
| 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 | 343 | 2010 |
| Handbook of graphical models M Maathuis, M Drton, S Lauritzen, M Wainwright CRC Press, 2018 | 308 | 2018 |
| Causal structure learning C Heinze-Deml, MH Maathuis, N Meinshausen Annual Review of Statistics and Its Application 5 (1), 371-391, 2018 | 293 | 2018 |
| High-dimensional consistency in score-based and hybrid structure learning P Nandy, A Hauser, MH Maathuis Annals of Statistics 46, 3151-3183, 2015 | 198 | 2015 |
| 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 | 174 | 2010 |
| A generalized back-door criterion MH Maathuis, D Colombo | 171 | 2015 |
| Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs E Perkovic, JC Textor, M Kalisch, MH Maathuis Journal of Machine Learning Research 18 (220), 1-62, 2018 | 165 | 2018 |
| Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example P Kassraian-Fard, C Matthis, JH Balsters, MH Maathuis, N Wenderoth Frontiers in psychiatry 7, 177, 2016 | 143 | 2016 |
| Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models MH Maathuis, E Perkovic, L Henckel Journal of the Royal Statistical Society: Series B, Statistical Methodology …, 2021 | 141* | 2021 |
| Causal Stability Ranking DJ Stekhoven, L Hennig, G Sveinbjörnsson, I Moraes, MH Maathuis, ... Bioinformatics 28, 2819-2823, 2012 | 104 | 2012 |
| A combined statistical approach and ground movement model for improving taxi time estimations at airports S Ravizza, JAD Atkin, MH Maathuis, EK Burke Journal of the Operational Research Society, 1347-1360, 2012 | 104 | 2012 |
| Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2 JS Huisman, J Scire, DC Angst, J Li, RA Neher, MH Maathuis, ... Elife 11, e71345, 2022 | 103 | 2022 |
| Estimating the effect of joint interventions from observational data in sparse high-dimensional settings P Nandy, MH Maathuis, TS Richardson | 102 | 2017 |
| A complete generalized adjustment criterion E Perković, J Textor, M Kalisch, MH Maathuis UAI 2015, 682-691, 2015 | 102 | 2015 |
Markus KalischETH ZürichVerified email at stat.math.ethz.ch
