Peter Bühlmann

research

∙ 09/18/2023

Invariant Probabilistic Prediction

In recent years, there has been a growing interest in statistical method...

0 Alexander Henzi, et al. ∙

research

∙ 09/05/2023

Distributionally Robust Machine Learning with Multi-source Data

Classical machine learning methods may lead to poor prediction performan...

0 Zhenyu Wang, et al. ∙

research

∙ 08/20/2023

Model Selection over Partially Ordered Sets

In problems such as variable selection and graph estimation, models are ...

0 Armeen Taeb, et al. ∙

research

∙ 07/18/2023

Causality-oriented robustness: exploiting general additive interventions

Since distribution shifts are common in real-world applications, there i...

0 Xinwei Shen, et al. ∙

research

∙ 04/02/2023

TSCI: two stage curvature identification for causal inference with invalid instruments

TSCI implements treatment effect estimation from observational data unde...

0 David Carl, et al. ∙

research

∙ 03/24/2023

repliclust: Synthetic Data for Cluster Analysis

We present repliclust (from repli-cate and clust-er), a Python package f...

0 Michael J. Zellinger, et al. ∙

research

∙ 02/11/2023

Confidence and Uncertainty Assessment for Distributional Random Forests

The Distributional Random Forest (DRF) is a recently introduced Random F...

0 Jeffrey Näf, et al. ∙

research

∙ 11/27/2022

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

We consider the problem of recovering the causal structure underlying ob...

0 Juan L Gamella, et al. ∙

research

∙ 10/20/2022

On the pitfalls of Gaussian likelihood scoring for causal discovery

We consider likelihood score based methods for causal discovery in struc...

0 Christoph Schultheiss, et al. ∙

research

∙ 10/13/2022

On the Identifiability and Estimation of Causal Location-Scale Noise Models

We study the class of location-scale or heteroscedastic noise models (LS...

0 Alexander Immer, et al. ∙

research

∙ 09/19/2022

Distributionally robust and generalizable inference

We discuss recently developed methods that quantify the stability and ge...

0 Dominik Rothenhäusler, et al. ∙

research

∙ 06/29/2022

Treatment Effect Estimation from Observational Network Data using Augmented Inverse Probability Weighting and Machine Learning

Causal inference methods for treatment effect estimation usually assume ...

0 Corinne Emmenegger, et al. ∙

research

∙ 05/18/2022

Ancestor regression in linear structural equation models

We present a new method for causal discovery in linear structural equati...

0 Christoph Schultheiss, et al. ∙

research

∙ 05/10/2022

Random Forests for Change Point Detection

We propose a novel multivariate nonparametric multiple change point dete...

0 Malte Londschien, et al. ∙

research

∙ 03/24/2022

Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables

Instrumental variables regression is a popular causal inference method f...

0 Zijian Guo, et al. ∙

research

∙ 11/29/2021

A Fast Non-parametric Approach for Causal Structure Learning in Polytrees

We study the problem of causal structure learning with no assumptions on...

0 Mona Azadkia, et al. ∙

research

∙ 11/08/2021

The Weighted Generalised Covariance Measure

We introduce a new test for conditional independence which is based on w...

0 Cyrill Scheidegger, et al. ∙

research

∙ 09/29/2021

Assessing the goodness of fit of linear regression via higher-order least squares

We introduce a simple diagnostic test for assessing the goodness of fit ...

0 Christoph Schultheiss, et al. ∙

research

∙ 08/31/2021

Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements

Traditionally, spline or kernel approaches in combination with parametri...

0 Corinne Emmenegger, et al. ∙

research

∙ 08/19/2021

Structure Learning for Directed Trees

Knowing the causal structure of a system is of fundamental interest in m...

11 Martin Emil Jakobsen, et al. ∙

research

∙ 08/02/2021

ricu: R's Interface to Intensive Care Data

Providing computational infrastructure for handling diverse intensive ca...

0 Nicolas Bennett, et al. ∙

research

∙ 07/12/2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Despite decades of clinical research, sepsis remains a global public hea...

2 Michael Moor, et al. ∙

research

∙ 04/30/2021

Efficient Multiple Testing Adjustment for Hierarchical Inference

Hierarchical inference in (generalized) regression problems is powerful ...

0 Claude Renaux, et al. ∙

research

∙ 01/29/2021

Regularizing Double Machine Learning in Partially Linear Endogenous Models

We estimate the linear coefficient in a partially linear model with conf...

0 Corinne Emmenegger, et al. ∙

research

∙ 01/20/2021

Distributional Anchor Regression

Prediction models often fail if train and test data do not stem from the...

0 Lucas Kook, et al. ∙

research

∙ 01/18/2021

Perturbations and Causality in Gaussian Models

Causal inference is understood to be a very challenging problem with obs...

0 Armeen Taeb, et al. ∙

research

∙ 01/06/2021

Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation

We consider estimation of undirected Gaussian graphical models and inver...

0 Solt Kovács, et al. ∙

research

∙ 10/29/2020

Domain adaptation under structural causal models

Domain adaptation (DA) arises as an important problem in statistical mac...

0 Yuansi Chen, et al. ∙

research

∙ 10/20/2020

Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries

As a classical and ever reviving topic, change point detection is often ...

0 Solt Kovács, et al. ∙

research

∙ 08/14/2020

Deconfounding and Causal Regularization for Stability and External Validity

We review some recent work on removing hidden confounding and causal reg...

0 Peter Bühlmann, et al. ∙

research

∙ 06/23/2020

Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)

In this discussion, we compare the choice of seeded intervals and that o...

0 Solt Kovács, et al. ∙

research

∙ 06/08/2020

Multicarving for high-dimensional post-selection inference

We consider post-selection inference for high-dimensional (generalized) ...

0 Christoph Schultheiss, et al. ∙

research

∙ 05/29/2020

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

We propose an adaptation of the Random Forest algorithm to estimate the ...

0 Domagoj Ćevid, et al. ∙

research

∙ 04/08/2020

Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding and Measurement Errors

Inferring causal relationships or related associations from observationa...

0 Zijian Guo, et al. ∙

research

∙ 02/16/2020

Seeded Binary Segmentation: A general methodology for fast and optimal change point detection

In recent years, there has been an increasing demand on efficient algori...

0 Solt Kovács, et al. ∙

research

∙ 11/05/2019

Stabilizing Variable Selection and Regression

We consider regression in which one predicts a response Y with a set of ...

0 Niklas Pfister, et al. ∙

research

∙ 09/24/2019

Double-estimation-friendly inference for high-dimensional misspecified models

All models may be wrong—but that is not necessarily a problem for infere...

0 Rajen D. Shah, et al. ∙

research

∙ 09/04/2019

Group Inference in High Dimensions with Applications to Hierarchical Testing

Group inference has been a long-standing question in statistics and the ...

0 Zijian Guo, et al. ∙

research

∙ 08/09/2019

Goodness-of-fit testing in high-dimensional generalized linear models

We propose a family of tests to assess the goodness-of-fit of a high-dim...

0 Jana Jankova, et al. ∙

research

∙ 07/11/2019

Change point detection for graphical models in presence of missing values

We propose estimation methods for change points in high-dimensional cova...

0 Malte Londschien, et al. ∙

research

∙ 12/19/2018

Invariance, Causality and Robustness

We discuss recent work for causal inference and predictive robustness in...

0 Peter Bühlmann, et al. ∙

research

∙ 12/10/2018

Estimating heterogeneous treatment effects in nonstationary time series with state-space models

Randomized trials and observational studies, more often than not, run ov...

0 Shu Li, et al. ∙

research

∙ 11/13/2018

Spectral Deconfounding and Perturbed Sparse Linear Models

Standard high-dimensional regression methods assume that the underlying ...

0 Domagoj Ćevid, et al. ∙

research

∙ 06/04/2018

groupICA: Independent component analysis for grouped data

We introduce groupICA, a novel independent component analysis (ICA) algo...

0 Niklas Pfister, et al. ∙

research

∙ 05/08/2018

Hierarchical inference for genome-wide association studies: a view on methodology with software

We provide a view on high-dimensional statistical inference for genome-w...

0 Claude Renaux, et al. ∙

research

∙ 01/18/2018

Anchor regression: heterogeneous data meets causality

This is a preliminary draft of "Anchor regression: heterogeneous data me...

0 Dominik Rothenhäusler, et al. ∙

research

∙ 03/01/2016

Kernel-based Tests for Joint Independence

We investigate the problem of testing whether d random variables, which ...

0 Niklas Pfister, et al. ∙

research

∙ 08/07/2015

Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams

We consider the problem of structure learning for bow-free acyclic path ...

0 Christopher Nowzohour, et al. ∙

research

∙ 11/25/2013

Score-based Causal Learning in Additive Noise Models

Given data sampled from a number of variables, one is often interested i...

0 Christopher Nowzohour, et al. ∙

research

∙ 11/14/2013

High-dimensional learning of linear causal networks via inverse covariance estimation

We establish a new framework for statistical estimation of directed acyc...

0 Po-Ling Loh, et al. ∙

Peter Bühlmann

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