Peter Bühlmann
YOU?
Author Swipe
View article: A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at Scale
A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at Scale Open
Intensive care departments generate vast multivariate time series data capturing the dynamic physiological states of critically ill patients. Despite advances in AI-driven clinical decision support, existing models remain limited. They are…
View article: A Residual Prediction Test for the Well-Specification of Linear Instrumental Variable Models
A Residual Prediction Test for the Well-Specification of Linear Instrumental Variable Models Open
The linear instrumental variable (IV) model is widely applied in observational studies. The corresponding assumptions are critical for valid causal inference, and hence, it is important to have tools to assess the model's well-specificatio…
View article: Natural Language-Based Synthetic Data Generation for Cluster Analysis
Natural Language-Based Synthetic Data Generation for Cluster Analysis Open
Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of the data sets, such as the overlap between clusters, or the v…
View article: Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning Open
We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learning (DML) and uses efficient machine learning instruments (M…
View article: A framework and analytical exploration for a data-driven update of the Sequential Organ Failure Assessment (SOFA) score in sepsis
A framework and analytical exploration for a data-driven update of the Sequential Organ Failure Assessment (SOFA) score in sepsis Open
We developed and validated a framework for a data-driven update to the SOFA to identify and classify organ dysfunction in suspected septic patients. This framework can be used to revise the SOFA score and its application to the identificat…
View article: Spectrally Deconfounded Random Forests
Spectrally Deconfounded Random Forests Open
We introduce a modification of Random Forests to estimate functions when unobserved confounding variables are present. The technique is tailored for high-dimensional settings with many observed covariates. We use spectral deconfounding tec…
View article: Causal chambers as a real-world physical testbed for AI methodology
Causal chambers as a real-world physical testbed for AI methodology Open
In some fields of artificial intelligence, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, whic…
View article: Spectral Deconfounding for High-Dimensional Sparse Additive Models
Spectral Deconfounding for High-Dimensional Sparse Additive Models Open
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 algorithms lead to biased estimates. This paper substantially…
View article: Ancestor regression in structural vector autoregressive models
Ancestor regression in structural vector autoregressive models Open
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 while retaining its favorable properties, i.e., explicit erro…
View article: Treatment effect estimation with observational network data using machine learning
Treatment effect estimation with observational network data using machine learning Open
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them. We develop augmented inverse…
View article: Causal Invariance Learning via Efficient Nonconvex Optimization
Causal Invariance Learning via Efficient Nonconvex Optimization Open
Identifying the causal relationship among variables from observational data is an important yet challenging task. This work focuses on identifying the direct causes of an outcome and estimating their magnitude, i.e., learning the causal ou…
View article: Invariant probabilistic prediction
Invariant probabilistic prediction Open
Summary In recent years, there has been growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of the related research focuses on point predictions with…
View article: AI-empowered perturbation proteomics for complex biological systems
AI-empowered perturbation proteomics for complex biological systems Open
The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomi…
View article: Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets
Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets Open
We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector te…
View article: Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning Open
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning i…
View article: The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology Open
In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited info…
View article: Ancestor regression in structural vector autoregressive models
Ancestor regression in structural vector autoregressive models Open
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 while retaining its favorable properties, i.e., explicit erro…
View article: Model selection over partially ordered sets
Model selection over partially ordered sets Open
In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be …
View article: Extrapolation-Aware Nonparametric Statistical Inference
Extrapolation-Aware Nonparametric Statistical Inference Open
We define extrapolation as any type of statistical inference on a conditional function (e.g., a conditional expectation or conditional quantile) evaluated outside of the support of the conditioning variable. This type of extrapolation occu…
View article: Distributional Robustness and Transfer Learning Through Empirical Bayes
Distributional Robustness and Transfer Learning Through Empirical Bayes Open
We consider the problem of statistical inference on parameters of a target population when auxiliary observations are available from related populations. We propose a flexible empirical Bayes approach that can be applied on top of any asym…
View article: Spectral Deconfounding for High-Dimensional Sparse Additive Models
Spectral Deconfounding for High-Dimensional Sparse Additive Models Open
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 algorithms lead to biased estimates. This paper substantially…
View article: Distributionally Robust and Generalizable Inference
Distributionally Robust and Generalizable Inference Open
We discuss recently developed methods that quantify the stability and generalizability of statistical findings under distributional changes. In many practical problems, the data is not drawn i.i.d. from the target population. For example, …
View article: Assessing the overall and partial causal well-specification of nonlinear additive noise models
Assessing the overall and partial causal well-specification of nonlinear additive noise models Open
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 can infer the causal effect even in cases of such misspeci…
View article: Invariant Probabilistic Prediction
Invariant Probabilistic Prediction Open
In recent years, there has been a growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of the related research focuses on point predictions with the s…
View article: Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation Open
Empirical risk minimization often performs poorly when the distribution of the target domain differs from those of source domains. To address such potential distribution shifts, we develop an unsupervised domain adaptation approach that le…
View article: Model Selection over Partially Ordered Sets
Model Selection over Partially Ordered Sets Open
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. Consequently, false positive error or false negative error can be spec…