论文标题
因果效应的分层估计量中的特征选择：潜在结果，因果图和结构方程的经验教训
Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
论文作者
论文摘要
估计平均因果效应的理想回归（如果有）是什么？我们在离散协变量的设置中研究了这个问题，从而得出了各种分层估计器的有限样本差异的表达式。这种方法阐明了许多广泛引用的结果的基本统计现象。我们的博览会结合了研究因果效应估计的三种不同的方法论传统的见解：潜在的结果，因果图和具有加性误差的结构模型。
What is the ideal regression (if any) for estimating average causal effects? We study this question in the setting of discrete covariates, deriving expressions for the finite-sample variance of various stratification estimators. This approach clarifies the fundamental statistical phenomena underlying many widely-cited results. Our exposition combines insights from three distinct methodological traditions for studying causal effect estimation: potential outcomes, causal diagrams, and structural models with additive errors.
