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no code implementations • 29 Jun 2022 • Corinne Emmenegger, Meta-Lina Spohn, Peter Bühlmann

Causal inference methods for treatment effect estimation usually assume independent experimental units.

2 code implementations • 10 May 2022 • Malte Londschien, Peter Bühlmann, Solt Kovács

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

1 code implementation • 24 Mar 2022 • Zijian Guo, Peter Bühlmann

Instrumental variables regression is a popular causal inference method for endogenous treatment.

no code implementations • 29 Nov 2021 • Mona Azadkia, Armeen Taeb, Peter Bühlmann

DAG-FOCI outputs the set of parents of a response variable of interest.

1 code implementation • 31 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.

1 code implementation • 19 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.

no code implementations • 12 Jul 2021 • Michael Moor, Nicolas Bennet, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt

Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.

1 code implementation • 29 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

1 code implementation • 20 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

no code implementations • 6 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

1 code implementation • 29 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.

no code implementations • 20 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.

no code implementations • 14 Aug 2020 • Peter Bühlmann, Domagoj Ćevid

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

no code implementations • 23 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

no code implementations • 29 May 2020 • Domagoj Ćevid, Loris Michel, Jeffrey Näf, Nicolai Meinshausen, Peter Bühlmann

Random Forests (Breiman, 2001) is a successful and widely used regression and classification algorithm.

1 code implementation • 8 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

1 code implementation • 5 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

2 code implementations • 9 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

1 code implementation • 11 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.

3 code implementations • 4 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.

2 code implementations • 18 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

no code implementations • 1 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.

no code implementations • 7 Aug 2015 • Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans, Peter Bühlmann

We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs).

no code implementations • 6 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

no code implementations • 25 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.

no code implementations • 14 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.

no code implementations • 6 Oct 2013 • Peter Bühlmann, Jonas Peters, Jan Ernest

We develop estimation for potentially high-dimensional additive structural equation models.

2 code implementations • 5 Jun 2013 • Jonas Peters, Peter Bühlmann

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

no code implementations • 11 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.

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