6.4k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH70 papers
- Centre de Recherche en Économie et StatistiqueFR4 papers
- Max Planck Institute for Intelligent SystemsDE4 papers
- University of ZurichCH4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- University of BernCH3 papers
- Carnegie Mellon UniversityUS2 papers
- Delft University of TechnologyNL2 papers
- Heidelberg UniversityDE2 papers
- Institute for Biomedical EngineeringCH2 papers
- Laboratoire d’Analyse et de Mathématiques AppliquéesFR2 papers
- Ludwig-Maximilians-Universität MünchenDE2 papers
5 papers · 1 filter
Higher-order least squares: assessing partial goodness of fit of linear causal models
Christoph Schultheiss, Peter Bühlmann, Ming Yuan
We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular,…
Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
Corinne Emmenegger, Peter Bühlmann
Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed-effects mo…
Reconstruction of the neutrino mass as a function of redshift
Christiane S. Lorenz, Lena Funcke, Matthias Löffler +1
We reconstruct the neutrino mass as a function of redshift, z, from current cosmological data using both standard binned priors and linear spline priors with variable knots. Using…
Regularizing Double Machine Learning in Partially Linear Endogenous Models
Corinne Emmenegger, Peter Bühlmann
The linear coefficient in a partially linear model with confounding variables can be estimated using double machine learning (DML). However, this DML estimator has a two-stage leas…
Distributional Anchor Regression
Lucas Kook, Beate Sick, Peter Bühlmann
Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable b…