16 citations · 31 across the 8 of their papers we have counts for
6 papers · 1 filter
Learning a directed acyclic graph with additive heteroscedastic errors
Xintao Xia, Li Chen, Yue Hu +1
This paper studies causal discovery for a directed acyclic graph under a structural equation model with additive heteroscedastic errors. We first establish new identifiability resu…
Stable Causal Discovery via Directed Acyclic Graph Aggregation
Yunan Wu, Yue Wang, Chunlin Li +1
Directed Acyclic Graphs (DAGs) are central to uncovering causal structure in complex systems, yet learning a single DAG from data is often challenging: model uncertainty, finite sa…
Discovery and inference of a causal network with hidden confounding
Li Chen, Chunlin Li, Xiaotong Shen +1
This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-bas…
Nonlinear Causal Discovery with Confounders
Chunlin Li, Xiaotong Shen, Wei Pan
This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive…
Inference of nonlinear causal effects with GWAS summary data
Ben Dai, Chunlin Li, Haoran Xue +2
Large-scale genome-wide association studies (GWAS) have offered an exciting opportunity to discover putative causal genes or risk factors associated with diseases by using SNPs as…
Inference for a Large Directed Acyclic Graph with Unspecified Interventions
Chunlin Li, Xiaotong Shen, Wei Pan
Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized…