Randomization, statistics, and causal inference.
Randomization, statistics, and causal inference.
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DOI：
10.1097/00001648-199011000-00003
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发表时间：
1990-11-01
期刊：
影响因子：
--
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Greenland, S
本文综述了统计在因果推理中的作用。特别注意的是需要随机化来证明传统统计的因果推论，以及需要随机抽样来证明描述性推论。在大多数流行病学研究中，随机化和随机抽样在研究队列的集合中很少或根本没有作用。因此，我的结论是，对传统统计的概率解释很少是合理的，而且这种解释可能会助长对非随机研究的误解。解决这个问题的可能方法包括减少对推理统计的强调，转而使用数据描述符，并采用基于比常用的概率模型更现实的统计技术。
This paper reviews the role of statistics in causal inference. Special attention is given to the need for randomization to justify causal inferences from conventional statistics, and the need for random sampling to justify descriptive inferences. In most epidemiologic studies, randomization and random sampling play little or no role in the assembly of study cohorts. I therefore conclude that probabilistic interpretations of conventional statistics are rarely justified, and that such interpretations may encourage misinterpretation of nonrandomized studies. Possible remedies for this problem include deemphasizing inferential statistics in favor of data descriptors, and adopting statistical techniques based on more realistic probability models than those in common use.