6.4k citations · 14.8k across the 73 of their papers we have counts for
80 papers
Distributional Extrapolation for Interactions
Marin Šola, Xinwei Shen, Peter Bühlmann
Predicting combinatorial effects from limited-range observations is a fundamental challenge in many scientific domains, including drug discovery and hyperparameter optimization. We…
Spectrally Deconfounded Gradient Boosting
Andrea Nava, Peter Bühlmann, Fabio Sigrist
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfo…
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann +1
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for a…
Ancestor regression in structural vector autoregressive models
Christoph Schultheiss, Markus Ulmer, Peter Bühlmann
We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series wh…
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…