Testing for Necessary and/or Sufficient Causation: Which Cases Are Relevant?
Testing for Necessary and/or Sufficient Causation: Which Cases Are Relevant?
复制标题
测试必要和/或充分因果关系：哪些案例相关？
DOI：
10.1093/pan/10.2.178
复制
发表时间：
2002
影响因子：
5.4
通讯作者：
Jason Seawright
中科院分区：
文献类型：
--
作者：
Jason Seawright
以前的研究人员认为，必要和/或充分的原因应该通过研究设计，只考虑有限的情况下，对独立和因变量的分数组合进行测试。我探讨了这些作者提出的设计的因果推理的效用，相比之下，“所有情况下的设计。”我发现，如果研究人员仔细和适当地定义人口，人口中的每一个案例都有助于因果推理，因此是有用的。以前的作者拒绝这一主张的基础上，认为保持不变的边际分布的任何因变量或自变量跨工作和备用假设。我认为，这种限制一般是不适当的，因此，从整个人口的样本分析是逻辑上站得住脚的。我还认为这种设计在统计上更有效。对两项著名研究的重新分析表明，从相关人群的所有病例中取样，比仅从经历结果的病例中取样，对假设产生更大的信心。
Previous researchers have argued that necessary and/or sufficient causes should be tested through research designs that consider only cases with limited combinations of scores on the independent and the dependent variables. I explore the utility for causal inference of the design proposed by these authors, as compared to an “All Cases Design.” I find that, if researchers define the population carefully and appropriately, each case in the population contributes to causal inference and is therefore useful. Previous authors reject this claim on the basis of a view that holds constant the marginal distribution of either the dependent or the independent variable across the working and the alternate hypotheses. I argue that this restriction is not generally appropriate, and hence, an analysis that samples from the entire population is logically defensible. I also argue that this design is more statistically efficient. A reanalysis of two well-known studies demonstrates that sampling from all cases in the relevant population produces greater confidence in the hypothesis than sampling only from cases that experience the outcome.