3 papers
cs.LG2026
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Zheng Li, Hao Zhang, Ruxin Wang +3
Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene…
stat.ML2026
Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
Zeyu Liu, Zheng Li, Feng Xie +3
We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all vari…
cs.LG2026
A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
Zheng Li, Feng Xie, Shenglan Nie +3
Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in h…