no code implementations • 20 Aug 2024 • Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton

Recent research has sought to bypass the combinatorial search by reformulating causal discovery as a continuous optimization problem, employing constraints that ensure the acyclicity of the graph.

1 code implementation • 9 Aug 2024 • Daniela Schkoda, Elina Robeva, Mathias Drton

In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects.

1 code implementation • 4 Jun 2024 • Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash

We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables.

no code implementations • 31 May 2024 • Daniele Tramontano, Mathias Drton, Jalal Etesami

Finally, we report on estimation heuristics based on the identification result, explore a generalization to models with feedback loops, and provide new results on the identifiability of the causal graph.

1 code implementation • 19 Jun 2023 • Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Steffen Sonntag, Martin Roth

We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods.

no code implementations • 23 Feb 2023 • Grigor Keropyan, David Strieder, Mathias Drton

As an alternative, we propose a new approach for PNL causal discovery that uses rank-based methods to estimate the functional parameters.

no code implementations • 21 Nov 2022 • Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee

Methods for learning such graphical models are well developed in the case where all variables are either continuous or discrete, including in high-dimensions.

1 code implementation • 13 Aug 2022 • Daniele Tramontano, Anthea Monod, Mathias Drton

In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs that are polytrees.

no code implementations • 1 Aug 2022 • Thijs van Ommen, Mathias Drton

The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix.

no code implementations • 10 Sep 2021 • Shiqing Yu, Mathias Drton, Ali Shojaie

Applications such as the analysis of microbiome data have led to renewed interest in statistical methods for compositional data, i. e., multivariate data in the form of probability vectors that contain relative proportions.

1 code implementation • 20 May 2021 • Wenyu Chen, Mathias Drton, Ali Shojaie

Ancestral relations between variables play an important role in causal modeling.

no code implementations • 24 Sep 2020 • Shiqing Yu, Mathias Drton, Ali Shojaie

Score matching provides a powerful tool for estimating densities with such intractable normalizing constants, but as originally proposed is limited to densities on $\mathbb{R}^m$ and $\mathbb{R}_+^m$.

no code implementations • 10 Jun 2020 • Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu

We consider the problem of structure learning for linear causal models based on observational data.

1 code implementation • 16 Dec 2019 • Lina Lin, Mathias Drton, Ali Shojaie

Our framework is inspired by a recent line of work that proposes de-biasing penalized estimators to perform inference for high-dimensional linear models with fixed effects only.

no code implementations • 26 Dec 2018 • Shiqing Yu, Mathias Drton, Ali Shojaie

The score matching method of Hyv\"arinen [2005] avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over $\mathbb{R}^m$.

2 code implementations • 9 Jul 2018 • Wenyu Chen, Mathias Drton, Y. Samuel Wang

Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances.

Methodology Computation

no code implementations • 7 Jun 2016 • Mathias Drton, Marloes H. Maathuis

A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest.

no code implementations • 29 Dec 2014 • Mathias Drton, Shaowei Lin, Luca Weihs, Piotr Zwiernik

We clarify how in this case real log-canonical thresholds can be computed using polyhedral geometry, and we show how to apply the general theory to the Laplace integrals associated with Gaussian latent tree and forest models.

no code implementations • 26 Sep 2014 • Dennis Leung, Mathias Drton

In (exploratory) factor analysis, the loading matrix is identified only up to orthogonal rotation.

no code implementations • 9 Aug 2014 • Michael A. Finegold, Mathias Drton

Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data.

no code implementations • NeurIPS 2012 • Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty

We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models.

2 code implementations • NeurIPS 2010 • Rina Foygel, Mathias Drton

Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications.

Statistics Theory Statistics Theory

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