6.4k citations · 14.8k across the 73 of their papers we have counts for
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Spectral Deconfounding for High-Dimensional Sparse Additive Models
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
Many high-dimensional data sets suffer from hidden confounding which affects both the predictors and the response of interest. In such situations, standard regression methods or al…
Assessing the overall and partial causal well-specification of nonlinear additive noise models
Christoph Schultheiss, Peter Bühlmann
We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we…
Model Selection over Partially Ordered Sets
Armeen Taeb, Peter Bühlmann, Venkat Chandrasekaran
In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as presence or absence of a variable or an edge. Consequentl…
Causality-oriented robustness: exploiting general noise interventions
Xinwei Shen, Peter Bühlmann, Armeen Taeb
Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, suc…
Natural Language-Based Synthetic Data Generation for Cluster Analysis
Michael J. Zellinger, Peter Bühlmann
Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of th…
Confidence and Uncertainty Assessment for Distributional Random Forests
Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann +1
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure,…