Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
存在潜变量时非参数因果效应估计的局部协变量选择
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 for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global network structure among variables. However, identifying the global structure can be unnecessary and inefficient, especially when our…