Nicolai Meinshausen
Professor of Statistics
Seminar for Statistics, ETH Zurich
Raemistrasse 101, 8092 Zurich
Email: last name @


Most publications and working papers are on Google Scholar.

Causality and Inhomogeneous Data

Christina Heinze-Deml, Jonas Peters and Nicolai Meinshausen (2017),
Invariant Causal Prediction for Nonlinear Models

Christina Heinze-Deml, Marloes Maathuis, and Nicolai Meinshausen (2017),
Causal Structure Learning
to appear in Annual Review of Statistics and its Applications

Dominik Rothenhaeusler, Peter Buehlmann, Nicolai Meinshausen (2017),
Causal Dantzig: fast inference in linear structural equation models with hidden variables under additive interventions

Jonas Peters, Peter Buehlmann and Nicolai Meinshausen (2016)
Causal inference using invariant prediction: identification and confidence intervals
Journal of the Royal Statistical Society, Series B (with discussion), 78, 947-1012
(PDF, arxiv)

Nicolai Meinshausena, Alain Hauser, Joris Mooij, Jonas Peters, Philip Versteeg and Peter Buehlmann (2016),
Methods for causal inference from gene perturbation experiments and validation
Proceedings of the National Academy of Sciences, 113 (27), 7361-7368

Dominik Rothenhaeusler, Christina Heinze, Jonas Peters and Nicolai Meinshausen (2015)
backShift: Learning causal cyclic graphs from unknown shift interventions
NIPS 2015, 1513-1521

Peter Buehlmann and Nicolai Meinshausen (2016)
Magging: maximin aggregation for inhomogeneous large-scale data
Proceedings of the IEEE 104, 126-135
(PDF, arxiv)

Nicolai Meinshausen and Peter Buehlmann (2015)
Maximin effects in inhomogeneous large-scale data
Annals of Statistics 43 (4), 1801-1830
(PDF, arxiv)

Dominik Rothenhaeusler, Nicolai Meinshausen and Peter Buehlmann (2016)
Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data
in Statistical Analysis for High-Dimensional Data, The Abel Symposium 2014, Springer, 255-277

High-dimensional Data Analysis and Machine Learning

Luca Weihs, Mathias Drton and Nicolai Meinshausen (2017)
Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence

Gian Thanei, Nicolai Meinshausen and Rajen Shah (2016)
The xyz algorithm for fast interaction search in high-dimensional data

Rajen Shah and Nicolai Meinshausen (2016)
Min-wise hashing for large-scale regression and classification with sparse data
(PDF, arxiv)

Aurelie Lozano, Nicolai Meinshausen, Eunho Yang (2016)
Minimum Distance Lasso for robust high-dimensional regression
Electronic Journal of Statistics 10 (1), 1296-1340

Ruben Dezeure, Peter Buehlmann, Lukas Meier and Nicolai Meinshausen (2015)
High-dimensional Inference: Confidence intervals, p-values and R-Software hdi
Statistical Science 30, 533-558
(PDF, arxiv),

Nicolai Meinshausen (2015)
Group-bound: confidence intervals for groups of variables in sparse high-dimensional regression without assumptions on the design
Journal of the Royal Statistical Society, Series B 77 (5), 923-945
(PDF, arxiv)

Rajen Shah and Nicolai Meinshausen (2014)
Random Intersection Trees
Journal of Machine Learning Research 15, 629-654
(PDF, arxiv)

Tze Choy and Nicolai Meinshausen (2014)
Sparse distance metric learning
Computational Statistics 29, 515-528

Nicolai Meinshausen (2013)
Sign-constrained least squares estimation for high-dimensional regression
Electronic Journal of Statistics 7, 1607-1631
(PDF, arxiv)

Nicolai Meinshausen (2011)
Partition Maps
Journal of Computational and Graphical Statistics 20 (4), 1007-1028
(PDF, R package)

Nicolai Meinshausen and Peter Buehlmann (2010)
Stability Selection (with discussion)
Journal of the Royal Statistical Society, Series B 72, 417-473
(PDF, arxiv)

Nicolai Meinshausen (2010)
Node Harvest
Annals of Applied Statistics 4 (4), 2049-2072
(PDF, arXiv, R package)

Nicolai Meinshausen (2009)
Forest Garrote
Electronic Journal of Statistics 3, 1288-1304
(PDF, arxiv)

Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2009)
P-values for high-dimensional regression
Journal of the American Statistical Association 104, 1671-1681
(PDF, arXiv)

Zhou Fang and Nicolai Meinshausen (2012)
Liso Isotone for High-Dimensional Additive Isotonic Regression
Journal of Computational and Graphical Statistics 21 (1), 72-91
(PDF, arxiv)

Nicolai Meinshausen and Bin Yu (2009)
Lasso-type recovery of sparse representations for high-dimensional data
Annals of Statistics 37 (1), 246-270
(PDF, arxiv)

Nicolai Meinshausen (2008)
A Note on the Lasso for Graphical Gaussian Model Selection
Statistics & Probability Letters 78 (7), 880-884

Nicolai Meinshausen (2007)
Relaxed Lasso
Computational Statistics and Data Analysis 52 (1), 374-393
(PDF, R-package)

Nicolai Meinshausen (2006)
Quantile Regression Forests
Journal of Machine Learning Research 7, 983-999
(PDF, R-package)

Nicolai Meinshausen and Peter Buehlmann (2006)
High dimensional graphs and variable selection with the Lasso
Annals of Statistics 34 (3), 1436-1462
An interview in Essential Science Indicators in January 2008.
(PDF, arxiv)

Applied Work

Malte Meinshausen, Elisabeth Vogel, Alexander Nauels, Katja Lorbacher, Nicolai Meinshausen, David M. Etheridge, Paul J. Fraser, Stephen A. Montzka, Peter J. Rayner Cathy M. Trudinger, Paul B. Krummel, Urs Beyerle, Josep G. Canadell, John S. Daniel, Ian G. Enting, Rachel M. Law, Chris R. Lunder, Simon O'Doherty, Ron G. Prinn, Stefan Reimann, Mauro Rubino, Guus J. M. Velders, Martin K. Vollmer, Ray H. J. Wang, and Ray Weiss (2017)
Historical greenhouse gas concentrations for climate modelling (CMIP6)
Geoscientific Model Development, 10, 2057-2116

Jonas R. Klasen, Elke Barbez, Lukas Meier, Nicolai Meinshausen, Peter Bühlmann, Maarten Koornneef, Wolfgang Busch and Korbinian Schneeberger (2016)
A multi-marker association method for genome-wide association studies without the need for population structure correction
Nature Communications, to appear

Malte Meinshausen, Louise Jeffery, Johannes Guetschow, Yann Robiou du Pont, Joeri Rogelj, Michiel Schaeffer, Niklas Hoehne, Michel den Elzen, Sebastian Oberthuer, and Nicolai Meinshausen (2015)
National post-2020 greenhouse gas targets and diversity-aware leadership
Nature Climate Change 5,1098–1106

Jorge Zeron-Medina, Xuting Wang, Emmanouela Repapi, Michelle R. Campbell, Dan Su, Francesc Castro-Giner, Benjamin Davies, Elisabeth F.P. Peterse, Natalia Sacilotto, Graeme J. Walker, Tamara Terzian, Ian P. Tomlinson, Neil F. Box, Nicolai Meinshausen, Sarah De Val, Douglas A. Bell, Gareth L. Bond (2013)
A Polymorphic p53 Response Element in KIT Ligand Influences Cancer Risk and Has Undergone Natural Selection
Cell 155 (2), 410-422

Daniel Rowlands, David Frame, Duncan Ackerley, Tolu Aina, Ben Booth, Carl Christensen, Matthew Collins, Nicholas Faull, Chris Forest, Benjamin Grandey, Edward Gryspeerdt, Eleanor Highwood, William Ingram, Sylvia Knight, Ana Lopez, Neil Massey, Frances McNamara, Nicolai Meinshausen, Claudio Piani, Suzanne Rosier, Benjamin Sanderson, Leonard Smith, Daith Stone, Milo Thurston, Kuniko Yamazaki, Hiro Yamazaki and Myles Allen (2012)
Broad range of 2050 warming from an observationally constrained large climate model ensemble
Nature Geoscience 5, 256-260

Malte Meinshausen, Nicolai Meinshausen, William Hare, Sarah Raper, Katja Frieler, Reto Knutti, David Frame and Myles Allen (2009)
Greenhouse-gas emission targets for limiting global warming to 2 C
Nature 458, 1158-1162
(Nature Editorial, News & Views)

M. Allen, D. Frame, K. Frieler, W. Hare, C. Huntingford, C. Jones, R. Knutti, J. Lowe, M. Meinshausen, N. Meinshausen, and S. Raper (2009)
The exit strategy
Nature Reports Climate Change, 56-58

Myles Allen, David Frame, Chris Huntingford, Chris Jones, Jason Lowe, Malte Meinshausen and Nicolai Meinshausen (2009)
Warming caused by cumulative carbon emissions towards the trillionth tonne
Nature 458, 1163-1166

Weng-Ping Chen, Charles Alcock, Tim Axelrod, Federica Bianco, Yong-Ik Byun, Hsiang-Kuang Chang, Kem Cook, Rahul Dave, Joseph Giammarco, D. Kim, Sun-Kung King, Typhoon Lee, Matthew Lehner, Chun-Che Lin, Lupin Lin, Jack Lissauer, Stuart Marshall, Nicolai Meinshausen, Soumen Mondal, Imke De Pater, Rodin Porrata, John Rice, Megan Schwamb, Andrew Wang, Shiang-Yu Wang, Chih-Yi Wen and Zhi-Wei Zhang (2006)
Search for small trans-Neptunian objects by the TAOS project
Proceedings of the International Astronomical Union 2, 65-68
Cambridge University Press

Multiple Testing

Nicolai Meinshausen and Marloes Maathuis and Peter Buehlmann (2011)
Optimality of the Westfall-Young permutation procedure for multiple testing under dependence
Annals of Statistics 39 (6), 3369-3391
(PDF, arxiv)

Nicolai Meinshausen (2008)
Hierarchical testing of variable importance
Biometrika 95 (2), 265-278

Nicolai Meinshausen (2006)
False discovery control for multiple tests of association under general dependence
Scandinavian Journal of Statistics 33 (2), 227-237

Nicolai Meinshausen and John Rice (2006)
Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
Annals of Statistics 34 (1), 373-393
(PDF, arxiv, R-package)

Nicolai Meinshausen and Peter Buehlmann (2005)
Lower bounds for the number of false null hypotheses for multiple testing of associations
Biometrika 92 (4), 893-907
(PDF, R-package)

Distributed Optimization

Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen (2017)
Preserving Differential Privacy Between Features in Distributed Estimation

Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann and Nicolai Meinshausen (2016)
Scalable Adaptive Stochastic Optimization Using Random Projections
NIPS 2016, 1750-1758

Christina Heinze, Brian McWilliams and Nicolai Meinshausen (2016)
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
AISTATS 2016 (PMLR) , 875-883

Dynamic Programming and Dual Monte Carlo methods

Nicolai Meinshausen, Peter Bickel and John Rice (2009)
Efficient Blind Search: Optimal Power of Detection under Computational Cost Constraints
Annals of Applied Statistics 3 (1), 38-60
(PDF, arxiv)

Nicolai Meinshausen and Ben Hambly (2004)
Monte carlo methods for the valuation of multiple exercise options
Mathematical Finance 14 (4) 557-583

Discussion Contributions

Nicolai Meinshausen (2013)
Discussion of "Grouping Strategies and Thresholding for High Dimension Linear Models"
Journal of Statistical Planning and Inference 143 (9), 1439-1440

Steffen Lauritzen and Nicolai Meinshausen (2012)
Discussion: Latent Variable Graphical Model Selection Via Convex Optimization
Annals of Statistics 40 (4), 1973-1977

Nicolai Meinshausen (2011)
Discussion of "Multiple Testing for Exploratory Research"
Statistical Science 26 (4), 601-603

Nicolai Meinshausen and Peter Buehlmann (2008)
Discussion of: Treelets -- An Adaptive Multi-Scale Basis for Sparse Unordered Data
Annals of Applied Statistics 2 (2), 478-481
(PDF, arxiv)

Nicolai Meinshausen, Guilherme Rocha and Bin Yu (2007)
Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig
Annals of Statistics 35 (6), 2373-2384
(PDF, arxiv)