Inference on a New Class of Sample Average Treatment Effects
成果类型：
Article
署名作者：
Sekhon, Jasjeet S.; Shem-Tov, Yotam
署名单位：
Yale University; Yale University; University of California System; University of California Los Angeles
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2020.1730854
发表日期：
2021
页码：
798-804
关键词：
摘要：
We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher's objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both accuracy and coverage can occur by the change of estimand, even asymptotically. Inference on SATE, even using a conservative confidence interval, provides incorrect coverage of SATT. We demonstrate the applicability of the new theoretical results using an empirical application with hundreds of online experiments with an average sample size of approximately 100 million observations per experiment. An R package, estCI, that implements all the proposed estimation procedures is available.for this article are available online.
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