4 papers
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…
TabCF: Distributional Control Function Estimation with Tabular Foundation Models
Geping Chen, Chunlin Li, Tianzhong Yang +2
Instrumental variable (IV) and control function (CF) methods are powerful tools for causal effect estimation in the presence of unmeasured confounding, yet most existing approaches…
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…