Causal inference from 2K factorial designs by using potential outcomes
Causal inference from 2K factorial designs by using potential outcomes
复制标题
使用潜在结果从 2K 因子设计进行因果推断
DOI：
10.1111/rssb.12085
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
D. Rubin
中科院分区：
文献类型：
--
作者：
Tirthankar Dasgupta;N. Pillai;D. Rubin
提出了一个从两级因子设计进行因果推断的框架，该框架使用潜在的结果来定义因果效应。本文探讨了单位水平治疗效应的非可加性对 Neyman 重复抽样方法（用于估计因果效应）以及 Fisher 随机化检验（对这些设计中尖锐零假设的影响）的影响。该框架允许从有限总体中进行统计推断，允许定义和估计除“平均阶乘效应”之外的估计值，并且比基于线性模型的普通最小二乘估计的推断程序更灵活。
A framework for causal inference from two‐level factorial designs is proposed, which uses potential outcomes to define causal effects. The paper explores the effect of non‐additivity of unit level treatment effects on Neyman's repeated sampling approach for estimation of causal effects and on Fisher's randomization tests on sharp null hypotheses in these designs. The framework allows for statistical inference from a finite population, permits definition and estimation of estimands other than ‘average factorial effects’ and leads to more flexible inference procedures than those based on ordinary least squares estimation from a linear model.
影响因子：
7
作者：
Collins, Linda M.;Dziak, John J.;Li, Runze
通讯作者：
Li, Runze