Improved Neymanian analysis for 2K factorial designs with binary outcomes

被引：0

作者：

Lu, Jiannan ^{[1
]}

机构：

[1] Microsoft Corp, Anal & Expt, One Microsoft Way, Redmond, WA 98052 USA

来源：

STATISTICA NEERLANDICA | 2019年 / 73卷 / 04期

关键词：

partial identification; potential outcome; randomization; robust inference; CAUSAL INFERENCE; BOUNDS;

D O I：

10.1111/stan.12186

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

2(K) factorial designs are widely adopted by statisticians and the broader scientific community. In this short note, under the potential outcomes framework, we adopt the partial identification approach and derive the sharp lower bound of the sampling variance of the estimated factorial effects, which leads to an "improved" Neymanian variance estimator that mitigates the overestimation issue suffered by the classic Neymanian variance estimator.

引用

页码：514 / 523

页数：10

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