26 citations · 65 across the 20 of their papers we have counts for
20 papers
Testing the Validity of Instrumental Variable Sets in Causal Additive Models with Non-Constant Effects
Xichen Guo, Feng Xie, Bingbing Tang +5
Instrumental variable (IV) methods are powerful for causal effect estimation with unmeasured confounding, but in practice researchers often face a set of candidate IVs whose validi…
Learning beyond Site Bias for OOD Generalization in Brain Networks
Yingxu Wang, Kunyu Zhang, Yanwu Yang +5
Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-si…
MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
Shicheng Fan, Nour Elhendawy, Jianle Sun +4
Causal representation learning (CRL) seeks to recover latent variables with identifiability guarantees, typically up to permutation and component-wise reparameterization under appr…
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Xinshuai Dong, Biwei Huang, Ignavier Ng +6
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a no…
Identifiable Latent Polynomial Causal Models Through the Lens of Change
Yuhang Liu, Zhen Zhang, Dong Gong +5
Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of id…
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
Feng Xie, Biwei Huang, Zhengming Chen +4
We investigate the task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal…
Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen +6
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe , an open-so…
Learning World Models with Identifiable Factorization
Yu-Ren Liu, Biwei Huang, Zhengmao Zhu +4
Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Diffe…
Identification of Nonlinear Latent Hierarchical Models
Lingjing Kong, Biwei Huang, Feng Xie +3
Identifying latent variables and causal structures from observational data is essential to many real-world applications involving biological data, medical data, and unstructured da…
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
Ruichu Cai, Zhiyi Huang, Wei Chen +2
Causal discovery with latent confounders is an important but challenging task in many scientific areas. Despite the success of some overcomplete independent component analysis (OIC…
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
Yujia Zheng, Ignavier Ng, Yewen Fan +1
A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables. Existing work focuses on specific families of distributi…
Scalable Causal Discovery with Score Matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +2
This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveragi…
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +2
Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in o…
Structure Learning with Continuous Optimization: A Sober Look and Beyond
Ignavier Ng, Biwei Huang, Kun Zhang
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 po…
Emerging Synergies in Causality and Deep Generative Models: A Survey
Guanglin Zhou, Shaoan Xie, Guang-Yuan Hao +7
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have prov…
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea +3
We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms perta…
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Haoyue Dai, Peter Spirtes, Kun Zhang
Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables a…
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Biwei Huang, Charles Jia Han Low, Feng Xie +2
Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems. In this paper, we consider a challenging…
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
Yuhang Liu, Zhen Zhang, Dong Gong +5
Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multip…
Identifying Weight-Variant Latent Causal Models
Yuhang Liu, Zhen Zhang, Dong Gong +5
The task of causal representation learning aims to uncover latent higher-level causal variables that affect lower-level observations. Identifying the true latent causal variables f…