Randomization-based causal inference from split-plot designs
نویسندگان
چکیده
منابع مشابه
A Paradox From Randomization-Based Causal Inference
Under the potential outcomes framework, causal effects are defined as comparisons between potential outcomes under treatment and control. To infer causal effects from randomized experiments, Neyman proposed to test the null hypothesis of zero average causal effect (Neyman’s null), and Fisher proposed to test the null hypothesis of zero individual causal effect (Fisher’s null). Although the subt...
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factorial and split-plot designs are two statistical procedures used to conduct agricultural field experiments. both designs are used frequently to quantify the main and interaction effects of two or more factors. however, split-plot design is recommended for specific situations depending on major and minor goals of the study, characteristics of the factors, and precision of statistical testing...
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T HIS provocative remark has been attributed to the famous industrial statistician, Cuthbert Daniel, by Box et al. (2005) in their well-known text on the design of experiments. Split-Plot experiments were invented by Fisher (1925) and their importance in industrial experimentation has been long recognized (Yates (1936)). It is also well known that many industrial experiments are fielded as spli...
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Split-plot experiments are appropriate when some factors are more difficult and/or expensive to change than others. They require two levels of randomization resulting in a non-independent error structure. The design of such experiments has garnered much recent attention, including work on exact D-optimal split-plot designs. However, many of these procedures rely on the a priori assumption that ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2018
ISSN: 0090-5364
DOI: 10.1214/17-aos1605