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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...
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08/17/2023
Partial Homoscedasticity in Causal Discovery with Linear Models
Recursive linear structural equation models and the associated directed ...
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08/03/2023
Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids
We consider structural equation models (SEMs), in which every variable i...
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07/06/2023
Probability Metrics for Tropical Spaces of Different Dimensions
The problem of comparing probability distributions is at the heart of ma...
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06/19/2023
: Generating Realistic Production Data for Benchmarking Causal Discovery
Algorithms for causal discovery have recently undergone rapid advances a...
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05/23/2023
Confidence Sets for Causal Orderings
Causal discovery procedures aim to deduce causal relationships among var...
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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 ...
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02/23/2023
Rank-Based Causal Discovery for Post-Nonlinear Models
Learning causal relationships from empirical observations is a central t...
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02/02/2023
Unpaired Multi-Domain Causal Representation Learning
The goal of causal representation learning is to find a representation o...
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11/21/2022
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Graphical models are an important tool in exploring relationships betwee...
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09/08/2022
Identifiability in Continuous Lyapunov Models
The recently introduced graphical continuous Lyapunov models provide a n...
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08/29/2022
On the Lasso for Graphical Continuous Lyapunov Models
Graphical continuous Lyapunov models offer a new perspective on modeling...
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08/24/2022
Testing Many and Possibly Singular Polynomial Constraints
We consider the problem of testing a null hypothesis defined by polynomi...
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08/13/2022
Learning Linear Non-Gaussian Polytree Models
In the context of graphical causal discovery, we adapt the versatile fra...
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08/01/2022
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
The observational characteristics of a linear structural equation model ...
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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 ...
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01/18/2022
Fine-grained network traffic prediction from coarse data
ICT systems provide detailed information on computer network traffic. Ho...
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01/12/2022
Half-Trek Criterion for Identifiability of Latent Variable Models
We consider linear structural equation models with latent variables and ...
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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...
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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 ...
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09/10/2021
Interaction Models and Generalized Score Matching for Compositional Data
Applications such as the analysis of microbiome data have led to renewed...
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08/15/2021
On Azadkia-Chatterjee's conditional dependence coefficient
In recent work, Azadkia and Chatterjee laid out an ingenious approach to...
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07/08/2021
Causal Structural Learning Via Local Graphs
We consider the problem of learning causal structures in sparse high-dim...
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06/10/2021
Confidence in Causal Discovery with Linear Causal Models
Structural causal models postulate noisy functional relations among a se...
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05/20/2021
Definite Non-Ancestral Relations and Structure Learning
In causal graphical models based on directed acyclic graphs (DAGs), dire...
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09/24/2020
Generalized Score Matching for General Domains
Estimation of density functions supported on general domains arises when...
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08/26/2020
On the power of Chatterjee rank correlation
Chatterjee (2020) introduced a simple new rank correlation coefficient t...
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07/21/2020
Causal Discovery with Unobserved Confounding and non-Gaussian Data
We consider the problem of recovering causal structure from multivariate...
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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...
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06/10/2020
Structure Learning for Cyclic Linear Causal Models
We consider the problem of structure learning for linear causal models b...
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04/08/2020
Directed Graphical Models and Causal Discovery for Zero-Inflated Data
Modern RNA sequencing technologies provide gene expression measurements ...
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03/12/2020
Existence and Uniqueness of the Kronecker Covariance MLE
In matrix-valued datasets the sampled matrices often exhibit correlation...
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12/16/2019
Statistical significance in high-dimensional linear mixed models
This paper concerns the development of an inferential framework for high...
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09/22/2019
Distribution-free consistent independence tests via Hallin's multivariate rank
This paper investigates the problem of testing independence of two rando...
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12/26/2018
Generalized Score Matching for Non-Negative Data
A common challenge in estimating parameters of probability density funct...
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12/14/2018
High dimensional independence testing with maxima of rank correlations
Testing mutual independence for high dimensional observations is a funda...
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10/26/2018
Algebraic tests of general Gaussian latent tree models
We consider general Gaussian latent tree models in which the observed va...
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07/19/2018
Nested Covariance Determinants and Restricted Trek Separation in Gaussian Graphical Models
Directed graphical models specify noisy functional relationships among a...
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07/09/2018
On Causal Discovery with Equal Variance Assumption
Prior work has shown that causal structure can be uniquely identified fr...
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05/14/2018
The Maximum Likelihood Threshold of a Path Diagram
Linear structural equation models postulate noisy linear relationships b...
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03/29/2018
High-Dimensional Causal Discovery Under non-Gaussianity
We consider data from graphical models based on a recursive system of li...
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03/29/2018
High-Dimensional Discovery Under non-Gaussianity
We consider data from graphical models based on a recursive system of li...
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02/18/2018
Graphical Models for Non-Negative Data Using Generalized Score Matching
A common challenge in estimating parameters of probability density funct...
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02/15/2018
Robust and sparse Gaussian graphical modeling under cell-wise contamination
Graphical modeling explores dependences among a collection of variables ...
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10/06/2017
Empirical Likelihood for Linear Structural Equation Models with Dependent Errors
We consider linear structural equation models that are associated with m...
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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 ...
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06/07/2016
Structure Learning in Graphical Modeling
A graphical model is a statistical model that is associated to a graph w...
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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...
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09/26/2014
Order-invariant prior specification in Bayesian factor analysis
In (exploratory) factor analysis, the loading matrix is identified only ...
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08/09/2014