6.4k citations · 14.8k across the 73 of their papers we have counts for
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The Weighted Generalised Covariance Measure
Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann
We introduce a new test for conditional independence which is based on what we call the weighted generalised covariance measure (WGCM). It is an extension of the recently introduce…
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…
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…