Search Results for author: Peter Bühlmann

Found 29 papers, 14 papers with code

Random Forests for Change Point Detection

2 code implementations10 May 2022 Malte Londschien, Peter Bühlmann, Solt Kovács

We propose a novel multivariate nonparametric multiple change point detection method using classifiers.

Change Point Detection

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

1 code implementation31 Aug 2021 Corinne Emmenegger, Peter Bühlmann

Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed-effects model for repeated measurements.

Machine Learning

Structure Learning for Directed Trees

1 code implementation19 Aug 2021 Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters

Furthermore, we study the identifiability gap, which quantifies how much better the true causal model fits the observational distribution, and prove that it is lower bounded by local properties of the causal model.

Regularizing Double Machine Learning in Partially Linear Endogenous Models

1 code implementation29 Jan 2021 Corinne Emmenegger, Peter Bühlmann

The linear coefficient in a partially linear model with confounding variables can be estimated using double machine learning (DML).

Methodology Statistics Theory Statistics Theory

Distributional Anchor Regression

1 code implementation20 Jan 2021 Lucas Kook, Beate Sick, Peter Bühlmann

In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors.

Methodology

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

no code implementations6 Jan 2021 Solt Kovács, Tobias Ruckstuhl, Helena Obrist, Peter Bühlmann

We consider estimation of undirected Gaussian graphical models and inverse covariances in high-dimensional scenarios by penalizing the corresponding precision matrix.

Methodology Computation

Domain adaptation under structural causal models

1 code implementation29 Oct 2020 Yuansi Chen, Peter Bühlmann

Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model.

Domain Adaptation

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

no code implementations20 Oct 2020 Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann

Towards solid understanding of our strategies, we investigate in detail the classical univariate Gaussian change in mean setup.

Change Point Detection

Deconfounding and Causal Regularization for Stability and External Validity

no code implementations14 Aug 2020 Peter Bühlmann, Domagoj Ćevid

We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint.

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

no code implementations23 Jun 2020 Solt Kovács, Housen Li, Peter Bühlmann

In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives.

Methodology Computation

Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding

1 code implementation8 Apr 2020 Zijian Guo, Domagoj Ćevid, Peter Bühlmann

Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding.

Methodology Statistics Theory Statistics Theory

Stabilizing Variable Selection and Regression

1 code implementation5 Nov 2019 Niklas Pfister, Evan G. Williams, Jonas Peters, Ruedi Aebersold, Peter Bühlmann

In particular, it is useful to distinguish between stable and unstable predictors, i. e., predictors which have a fixed or a changing functional dependence on the response, respectively.

Methodology Applications

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

2 code implementations9 Aug 2019 Jana Janková, Rajen D. Shah, Peter Bühlmann, Richard J. Samworth

We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model.

Methodology Statistics Theory Statistics Theory

Change point detection for graphical models in the presence of missing values

1 code implementation11 Jul 2019 Malte Londschien, Solt Kovács, Peter Bühlmann

We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values.

Change Point Detection Imputation +2

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

3 code implementations4 Jun 2018 Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.

Causal Inference EEG

Anchor regression: heterogeneous data meets causality

2 code implementations18 Jan 2018 Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann, Jonas Peters

If anchor regression and least squares provide the same answer (anchor stability), we establish that OLS parameters are invariant under certain distributional changes.

Methodology

Kernel-based Tests for Joint Independence

no code implementations1 Mar 2016 Niklas Pfister, Peter Bühlmann, Bernhard Schölkopf, Jonas Peters

Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation.

Causal Discovery

Causal inference using invariant prediction: identification and confidence intervals

no code implementations6 Jan 2015 Jonas Peters, Peter Bühlmann, Nicolai Meinshausen

In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables.

Methodology

Score-based Causal Learning in Additive Noise Models

no code implementations25 Nov 2013 Christopher Nowzohour, Peter Bühlmann

Given data sampled from a number of variables, one is often interested in the underlying causal relationships in the form of a directed acyclic graph.

Model Selection

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

no code implementations14 Nov 2013 Po-Ling Loh, Peter Bühlmann

We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model.

Structural Intervention Distance (SID) for Evaluating Causal Graphs

2 code implementations5 Jun 2013 Jonas Peters, Peter Bühlmann

To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).

Causal Inference

Identifiability of Gaussian structural equation models with equal error variances

no code implementations11 May 2012 Jonas Peters, Peter Bühlmann

In this work, we prove full identifiability if all noise variables have the same variances: the directed acyclic graph can be recovered from the joint Gaussian distribution.

Causal Inference

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