解决高维或共线数据下因果效应估计难题，提出新选择器提升准确性。
PCM Selector: Penalized Covariate-Mediator Selection Operator for Evaluating Linear Causal Effects
- 两阶段惩罚回归法，结合中介变量筛选
- 在高维/共线场景下实现更小偏差的因果效应估计
- 适合处理无法观测混杂变量或数据复杂的情况
针对可由线性结构方程模型描述的数据生成过程，在无法观测满足后门准则的协变量，或虽可观测但因多重共线性/高维问题导致标准统计方法失效的情况下，本文提出一种新型两阶段惩罚回归方法——惩罚协变量-中介选择算子（PCM Selector），用于估计因果效应。与现有惩罚回归分析不同，当存在中间变量时，该方法能提供因果效应的一致或更低偏差估计。此外，PCM Selector还提供中间变量的选择程序，使因果效应估计精度优于传统后门准则。
原文摘要 · Abstract (English)
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 penalized covariate-mediator selection operator (PCM Selector), to estimate the causal effects in such scenarios. Unlike existing penalized regression analyses, when a set of intermediate variables is available, PCM Selector provides a consistent or less biased estimator of the causal effect. In addition, PCM Selector provides a variable selection procedure for intermediate variables to obtain better estimation accuracy of the causal effects than does the back-door criterion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。