PCM Selector: Penalized Covariate-Mediator Selection Operator for Evaluating Linear Causal Effects
PCM 选择器:用于线性因果效应评估的惩罚协变量与中介选择方法
For a data-generating process for random variables that can be described with a linear structural equation model, we consider a situation in which (i) a set of covariates satisfying the back-door criterion cannot be observed or (ii) such a set can be observed, but standard statistical estimation methods cannot be applied to estimate causal effects because of multicollinearity/high-dimensional data problems. We propose a novel two-stage penalized regression approach, the…