论文标题
利用Fisher随机测试使用置信分配：推理，组合和融合学习
Leveraging the Fisher randomization test using confidence distributions: inference, combination and fusion learning
论文作者
论文摘要
Fisher随机测试（FRT）的灵活性和广泛的适用性使其成为评估来自现代随机实验的因果关系的有吸引力的工具，这些实验的大小和复杂性增加。本文通过建立与置信分布的联系为FRT提供了一个理论的推论框架，从而开发了（i）FRT倒置的明确过程，以生成具有保证覆盖的置信区间，（ii）将FRT与多个独立实验结合的通用和特定方法与多个独立的实验相结合的理论保证和（iii）的效果效果。我们的发展与有限的样本设置有关，但直接扩展到大型样品。模拟和案例示例证明了这些新发展的好处。
The flexibility and wide applicability of the Fisher randomization test (FRT) makes it an attractive tool for assessment of causal effects of interventions from modern-day randomized experiments that are increasing in size and complexity. This paper provides a theoretical inferential framework for FRT by establishing its connection with confidence distributions Such a connection leads to development of (i) an unambiguous procedure for inversion of FRTs to generate confidence intervals with guaranteed coverage, (ii) generic and specific methods to combine FRTs from multiple independent experiments with theoretical guarantees and (iii) new insights on the effect of size of the Monte Carlo sample on the results of FRT. Our developments pertain to finite sample settings but have direct extensions to large samples. Simulations and a case example demonstrate the benefit of these new developments.
