A Potential Tale of Two-by-Two Tables From Completely Randomized Experiments

• 成果类型: Article
• 署名作者: Ding, Peng; Dasgupta, Tirthankar
• 署名单位: Harvard University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.995796
• 发表日期: 2016
• 页码: 157-168
• 关键词: 2x2 table; inference; INFORMATION; principles

摘要

Causal inference in completely randomized treatment-control studies with binary outcomes is discussed from Fisherian, Neymanian, and Bayesian perspectives, using the potential outcomes model. A randomization-based justification of Fisher's exact test is provided. Arguing that the crucial assumption of constant causal effect is often unrealistic, and holds only for extreme cases, some new asymptotic and Bayesian inferential procedures are proposed. The proposed procedures exploit the intrinsic nonadditivity of unit-level causal effects, can be applied to linear and nonlinear estimands, and dominate the existing methods, as verified theoretically and also through simulation studies. Supplementary materials for this article are available online.