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Found 22 papers, 8 papers with code
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
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.
Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects.
Causal Effect Identification in LiNGAM Models with Latent Confounders
We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables.
Parameter identification in linear non-Gaussian causal models under general confounding
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.
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
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.
Rank-Based Causal Discovery for Post-Nonlinear Models
As an alternative, we propose a new approach for PNL causal discovery that uses rank-based methods to estimate the functional parameters.
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Methods for learning such graphical models are well developed in the case where all variables are either continuous or discrete, including in high-dimensions.
Learning Linear Non-Gaussian Polytree Models
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.
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix.
Interaction Models and Generalized Score Matching for Compositional Data
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.
Definite Non-Ancestral Relations and Structure Learning
Ancestral relations between variables play an important role in causal modeling.
Generalized Score Matching for General Domains
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 $\mathbb{R}_+^m$.
Structure Learning for Cyclic Linear Causal Models
We consider the problem of structure learning for linear causal models based on observational data.
Statistical significance in high-dimensional linear mixed models
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.
Generalized Score Matching for Non-Negative Data
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 $\mathbb{R}^m$.
On Causal Discovery with Equal Variance Assumption
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
Structure Learning in Graphical Modeling
A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest.
Marginal likelihood and model selection for Gaussian latent tree and forest models
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.
Order-invariant prior specification in Bayesian factor analysis
In (exploratory) factor analysis, the loading matrix is identified only up to orthogonal rotation.
Robust Graphical Modeling with t-Distributions
Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data.
Nonparametric Reduced Rank Regression
We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models.
Extended Bayesian Information Criteria for Gaussian Graphical Models
Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications.
Statistics Theory Statistics Theory
