Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments

• 成果类型: Article
• 署名作者: Mukerjee, Rahul; Dasgupta, Tirthankar; Rubin, Donald B.
• 署名单位: Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; Harvard University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1294076
• 发表日期: 2018
• 页码: 868-881
• 关键词: REGRESSION ADJUSTMENTS; randomization analysis; covariate balance; estimators; variance; DESIGN

摘要

This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated sampling approach that integrates such causal inference with finite population survey sampling, an inferential framework is developed for general mechanisms of assigning experimental units to multiple treatments. This framework extends classical methods by allowing the possibility of randomization restrictions and unequal replications. Novel conditions that are milder than strict additivity of treatment effects, yet permit unbiased estimation of the finite population sampling variance of any treatment contrast estimator, are derived. The consequences of departures from such conditions are also studied under the criterion of minimax bias, and a new justification for using the Neymanian conservative sampling variance estimator in experiments is provided. The proposed approach can readily be extended to the case of treatments with a general factorial structure.