Bühlmann, Peter

MissForest—Non-parametric missing value imputation for mixed-type data

Stekhoven, D. J., & Bühlmann, P. (2012) · Bioinformatics

Statistical Methods and Bayesian InferenceStatistical Methods and Inference

Cited by 6,156

High-dimensional graphs and variable selection with the Lasso

Meinshausen, N., & Bühlmann, P. (2006) · The Annals of Statistics

Statistical Methods and InferenceSparse and Compressive Sensing Techniques

Cited by 2,521

Predicting causal effects in large-scale systems from observational data

Maathuis, M. H., Colombo, D., Kalisch, M., & Bühlmann, P. (2010) · Nature Methods

Advanced Causal Inference TechniquesBayesian Modeling and Causal Inference

Cited by 217

Methods for causal inference from gene perturbation experiments and validation

Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P. (2016) · Proceedings of the National Academy of Sciences

Gene Regulatory Network AnalysisBayesian Modeling and Causal Inference

Cited by 140

Predicting sepsis using deep learning across international sites: a retrospective development and validation study

Moor, M., Bennett, N., Plečko, D., Horn, M., Rieck, B., Meinshausen, N., Bühlmann, P., & Borgwardt, K. (2023) · eClinicalMedicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 118