6.4k citations · 14.8k across the 73 of their papers we have counts for
45 papers · 1 filter
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…
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…
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…