12 citations · 27 across the 7 of their papers we have counts for
7 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…
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…
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…
Identifiability of causal effects with non-Gaussianity and auxiliary covariates
Kang Shuai, Shanshan Luo, Yue Zhang +2
Assessing causal effects in the presence of unmeasured confounding is challenging. Although auxiliary variables, such as instrumental variables, are commonly used to identify causa…
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…
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Feng Xie, Ruichu Cai, Biwei Huang +3
Causal discovery aims to recover causal structures or models underlying the observed data. Despite its success in certain domains, most existing methods focus on causal relations b…
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Yan Zeng, Shohei Shimizu, Ruichu Cai +3
Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the si…