6 papers
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…
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…
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…
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
Zheng Li, Xichen Guo, Feng Xie +3
Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates f…
Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
Xichen Guo, Zheng Li, Biwei Huang +3
We address the issue of the testability of instrumental variables derived from observational data. Most existing testable implications are centered on scenarios where the treatment…
Local Causal Structure Learning in the Presence of Latent Variables
Feng Xie, Zheng Li, Peng Wu +3
Discovering causal relationships from observational data, particularly in the presence of latent variables, poses a challenging problem. While current local structure learning meth…