b'Mathias Drton'

research

∙ 09/08/2023

Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances

Inferring the effect of interventions within complex systems is a fundam...

0 David Strieder, et al. ∙

research

∙ 08/17/2023

Partial Homoscedasticity in Causal Discovery with Linear Models

Recursive linear structural equation models and the associated directed ...

0 Jun Wu, et al. ∙

research

∙ 08/03/2023

Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids

We consider structural equation models (SEMs), in which every variable i...

0 Mathias Drton, et al. ∙

research

∙ 07/06/2023

Probability Metrics for Tropical Spaces of Different Dimensions

The problem of comparing probability distributions is at the heart of ma...

0 Roan Talbut, et al. ∙

research

∙ 06/19/2023

: Generating Realistic Production Data for Benchmarking Causal Discovery

Algorithms for causal discovery have recently undergone rapid advances a...

0 Konstantin Göbler, et al. ∙

research

∙ 05/23/2023

Confidence Sets for Causal Orderings

Causal discovery procedures aim to deduce causal relationships among var...

0 Y. Samuel Wang, et al. ∙

research

∙ 03/30/2023

Discussion of "A note on universal inference" by Timmy Tse and Anthony Davison

Invited discussion for Stat of "A note on universal inference" by Timmy ...

0 Mathias Drton, et al. ∙

research

∙ 02/23/2023

Rank-Based Causal Discovery for Post-Nonlinear Models

Learning causal relationships from empirical observations is a central t...

0 Grigor Keropyan, et al. ∙

research

∙ 02/02/2023

Unpaired Multi-Domain Causal Representation Learning

The goal of causal representation learning is to find a representation o...

0 Nils Sturma, et al. ∙

research

∙ 11/21/2022

High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data

Graphical models are an important tool in exploring relationships betwee...

0 Konstantin Göbler, et al. ∙

research

∙ 09/08/2022

Identifiability in Continuous Lyapunov Models

The recently introduced graphical continuous Lyapunov models provide a n...

0 Philipp Dettling, et al. ∙

research

∙ 08/29/2022

On the Lasso for Graphical Continuous Lyapunov Models

Graphical continuous Lyapunov models offer a new perspective on modeling...

0 Philipp Dettling, et al. ∙

research

∙ 08/24/2022

Testing Many and Possibly Singular Polynomial Constraints

We consider the problem of testing a null hypothesis defined by polynomi...

0 Nils Sturma, et al. ∙

research

∙ 08/13/2022

Learning Linear Non-Gaussian Polytree Models

In the context of graphical causal discovery, we adapt the versatile fra...

10 Daniele Tramontano, et al. ∙

research

∙ 08/01/2022

Graphical Representations for Algebraic Constraints of Linear Structural Equations Models

The observational characteristics of a linear structural equation model ...

0 Thijs van Ommen, et al. ∙

research

∙ 03/13/2022

On the choice of the splitting ratio for the split likelihood ratio test

The recently introduced framework of universal inference provides a new ...

0 David Strieder, et al. ∙

research

∙ 01/18/2022

Fine-grained network traffic prediction from coarse data

ICT systems provide detailed information on computer network traffic. Ho...

0 Krzysztof Rusek, et al. ∙

research

∙ 01/12/2022

Half-Trek Criterion for Identifiability of Latent Variable Models

We consider linear structural equation models with latent variables and ...

0 Rina Foygel Barber, et al. ∙

research

∙ 12/20/2021

Third-Order Moment Varieties of Linear Non-Gaussian Graphical Models

In this paper we study linear non-Gaussian graphical models from the per...

0 Carlos Améndola, et al. ∙

research

∙ 11/30/2021

Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence

Defining multivariate generalizations of the classical univariate ranks ...

0 Hongjian Shi, et al. ∙

research

∙ 09/10/2021

Interaction Models and Generalized Score Matching for Compositional Data

Applications such as the analysis of microbiome data have led to renewed...

0 Shiqing Yu, et al. ∙

research

∙ 08/15/2021

On Azadkia-Chatterjee's conditional dependence coefficient

In recent work, Azadkia and Chatterjee laid out an ingenious approach to...

0 Hongjian Shi, et al. ∙

research

∙ 07/08/2021

Causal Structural Learning Via Local Graphs

We consider the problem of learning causal structures in sparse high-dim...

0 Wenyu Chen, et al. ∙

research

∙ 06/10/2021

Confidence in Causal Discovery with Linear Causal Models

Structural causal models postulate noisy functional relations among a se...

0 David Strieder, et al. ∙

research

∙ 05/20/2021

Definite Non-Ancestral Relations and Structure Learning

In causal graphical models based on directed acyclic graphs (DAGs), dire...

0 Wenyu Chen, et al. ∙

research

∙ 09/24/2020

Generalized Score Matching for General Domains

Estimation of density functions supported on general domains arises when...

0 Shiqing Yu, et al. ∙

research

∙ 08/26/2020

On the power of Chatterjee rank correlation

Chatterjee (2020) introduced a simple new rank correlation coefficient t...

0 Hongjian Shi, et al. ∙

research

∙ 07/21/2020

Causal Discovery with Unobserved Confounding and non-Gaussian Data

We consider the problem of recovering causal structure from multivariate...

0 Y. Samuel Wang, et al. ∙

research

∙ 07/04/2020

Rate-optimality of consistent distribution-free tests of independence based on center-outward ranks and signs

Rank correlations have found many innovative applications in the last de...

0 Hongjian Shi, et al. ∙

research

∙ 06/10/2020

Structure Learning for Cyclic Linear Causal Models

We consider the problem of structure learning for linear causal models b...

0 Carlos Améndola, et al. ∙

research

∙ 04/08/2020

Directed Graphical Models and Causal Discovery for Zero-Inflated Data

Modern RNA sequencing technologies provide gene expression measurements ...

0 Shiqing Yu, et al. ∙

research

∙ 03/12/2020

Existence and Uniqueness of the Kronecker Covariance MLE

In matrix-valued datasets the sampled matrices often exhibit correlation...

0 Mathias Drton, et al. ∙

research

∙ 12/16/2019

Statistical significance in high-dimensional linear mixed models

This paper concerns the development of an inferential framework for high...

0 Lina Lin, et al. ∙

research

∙ 09/22/2019

Distribution-free consistent independence tests via Hallin's multivariate rank

This paper investigates the problem of testing independence of two rando...

0 Hongjian Shi, et al. ∙

research

∙ 12/26/2018

Generalized Score Matching for Non-Negative Data

A common challenge in estimating parameters of probability density funct...

0 Shiqing Yu, et al. ∙

research

∙ 12/14/2018

High dimensional independence testing with maxima of rank correlations

Testing mutual independence for high dimensional observations is a funda...

0 Mathias Drton, et al. ∙

research

∙ 10/26/2018

Algebraic tests of general Gaussian latent tree models

We consider general Gaussian latent tree models in which the observed va...

0 Dennis Leung, et al. ∙

research

∙ 07/19/2018

Nested Covariance Determinants and Restricted Trek Separation in Gaussian Graphical Models

Directed graphical models specify noisy functional relationships among a...

0 Mathias Drton, et al. ∙

research

∙ 07/09/2018

On Causal Discovery with Equal Variance Assumption

Prior work has shown that causal structure can be uniquely identified fr...

0 Mathias Drton, et al. ∙

research

∙ 05/14/2018

The Maximum Likelihood Threshold of a Path Diagram

Linear structural equation models postulate noisy linear relationships b...

0 Mathias Drton, et al. ∙

research

∙ 03/29/2018

High-Dimensional Causal Discovery Under non-Gaussianity

We consider data from graphical models based on a recursive system of li...

0 Y. Samuel Wang, et al. ∙

research

∙ 03/29/2018

High-Dimensional Discovery Under non-Gaussianity

We consider data from graphical models based on a recursive system of li...

0 Y. Samuel Wang, et al. ∙

research

∙ 02/18/2018

Graphical Models for Non-Negative Data Using Generalized Score Matching

A common challenge in estimating parameters of probability density funct...

0 Shiqing Yu, et al. ∙

research

∙ 02/15/2018

Robust and sparse Gaussian graphical modeling under cell-wise contamination

Graphical modeling explores dependences among a collection of variables ...

0 Shota Katayama, et al. ∙

research

∙ 10/06/2017

Empirical Likelihood for Linear Structural Equation Models with Dependent Errors

We consider linear structural equation models that are associated with m...

0 Y. Samuel Wang, et al. ∙

research

∙ 08/18/2017

Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence

The need to test whether two random vectors are independent has spawned ...

0 Luca Weihs, et al. ∙

research

∙ 06/07/2016

Structure Learning in Graphical Modeling

A graphical model is a statistical model that is associated to a graph w...

0 Mathias Drton, et al. ∙

research

∙ 12/29/2014

Marginal likelihood and model selection for Gaussian latent tree and forest models

Gaussian latent tree models, or more generally, Gaussian latent forest m...

0 Mathias Drton, et al. ∙

research

∙ 09/26/2014

Order-invariant prior specification in Bayesian factor analysis

In (exploratory) factor analysis, the loading matrix is identified only ...

0 Dennis Leung, et al. ∙

research

∙ 08/09/2014

Robust Graphical Modeling with t-Distributions

Graphical Gaussian models have proven to be useful tools for exploring n...

0 Michael A. Finegold, et al. ∙

Mathias Drton

Featured Co-authors