Mathias Drton
26 papers · 2008–2025 · 7 conferences · across top CS/AI conferences
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causal discovery
(6)
graphical model
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causal inference
(4)
structural equation model
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gaussian graphical model
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exponential family
(2)
parameter estimation
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non-negative datum
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causal effect
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structure learning
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structural causal model
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causal graph
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covariance matrix
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nonparametric regression
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score matching
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linear causal model
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Papers
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles
UAI 2025
Robust Score Matching
AISTATS 2025
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
ICML 2025
Nonlinear Causal Discovery for Grouped Data
UAI 2025
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
CLEAR 2024
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
PGM 2024
Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity
PGM 2024
Causal Effect Identification in LiNGAM Models with Latent Confounders
ICML 2024
Dual Likelihood for Causal Inference under Structure Uncertainty
CLEAR 2024
On the Lasso for Graphical Continuous Lyapunov Models
CLEAR 2024
Unpaired Multi-Domain Causal Representation Learning
NIPS 2023
Rank-Based Causal Discovery for Post-Nonlinear Models
AISTATS 2023
Directed Graphical Models and Causal Discovery for Zero-Inflated Data
CLEAR 2023
Causal Discovery with Unobserved Confounding and Non-Gaussian Data
JMLR 2023
Learning linear non-Gaussian polytree models
UAI 2022
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
PGM 2022
Confidence in causal discovery with linear causal models
UAI 2021
Structure Learning for Cyclic Linear Causal Models
UAI 2020
Generalized Score Matching for Non-Negative Data
JMLR 2019
Algebraic tests of general Gaussian latent tree models
NIPS 2018
Graphical Models for Non-Negative Data Using Generalized Score Matching
AISTATS 2018
PC Algorithm for Nonparanormal Graphical Models
JMLR 2013
Nonparametric Reduced Rank Regression
NIPS 2012
Extended Bayesian Information Criteria for Gaussian Graphical Models
NIPS 2010
Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
JMLR 2009
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
JMLR 2008