Search Results for author:
Found 19 papers, 14 papers with code
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts.
Local Causal Discovery with Linear non-Gaussian Cyclic Models
Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies solely on a single target variable.
Federated Causal Discovery from Heterogeneous Data
This discrepancy has motivated the development of federated causal discovery (FCD) approaches.
Causal Representation Learning from Multiple Distributions: A General Setting
We show that under the sparsity constraint on the recovered graph over the latent variables and suitable sufficient change conditions on the causal influences, interestingly, one can recover the moralized graph of the underlying directed acyclic graph, and the recovered latent variables and their relations are related to the underlying causal model in a specific, nontrivial way.
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables.
Structure Learning with Continuous Optimization: A Sober Look and Beyond
This paper investigates in which cases continuous optimization for directed acyclic graph (DAG) structure learning can and cannot perform well and why this happens, and suggests possible directions to make the search procedure more reliable.
Truncated Matrix Power Iteration for Differentiable DAG Learning
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
We show that under specific instantiations of such constraints, the independent latent sources can be identified from their nonlinear mixtures up to a permutation and a component-wise transformation, thus achieving nontrivial identifiability of nonlinear ICA without auxiliary variables.
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations.
STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity
In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering.
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness.
gCastle: A Python Toolbox for Causal Discovery
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered.
On the Convergence of Continuous Constrained Optimization for Structure Learning
Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a continuous optimization problem by leveraging an algebraic characterization of acyclicity.
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Extensive experiments validate the effectiveness of our proposed method and show that the DAG-penalized likelihood objective is indeed favorable over the least squares one with the hard DAG constraint.
A Graph Autoencoder Approach to Causal Structure Learning
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees.
Masked Gradient-Based Causal Structure Learning
This paper studies the problem of learning causal structures from observational data.
Causal Discovery with Reinforcement Learning
The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.
