Causal inference from 2K factorial designs by using potential outcomes

• 成果类型: Article
• 署名作者: Dasgupta, Tirthankar; Pillai, Natesh S.; Rubin, Donald B.
• 署名单位: Harvard University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12085
• 发表日期: 2015
• 页码: 727-753
• 关键词: randomization analysis

摘要

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.