Why Causation Need not Follow From Statistical Association: Boundary Conditions for the Evaluation of Generative and Preventive Causal Powers
Why Causation Need not Follow From Statistical Association: Boundary Conditions for the Evaluation of Generative and Preventive Causal Powers
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为什么因果关系不需要遵循统计关联：评估生成和预防因果能力的边界条件
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
P. Cheng
中科院分区：
文献类型：
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作者：
Melissa Wu;P. Cheng
在实验设计中，一个默认的原则是，为了测试一个候选原因c(即操纵)是否阻止了一个效应e，e必须至少在某些时候发生而不引入c。这个原则是在c是否产生e的测试中避免天花板效应这一显性原则的预防性类比。采用协变方法或幂方法的因果推理的心理学模型，以及其他问题，都无法解释这些原理。本文报告了一个实验，它演示了这些原理在非辅助性推理中的操作。研究结果支持了根据POWER PC理论对这些原理的解释，该理论整合了以前的方法，以克服各自削弱的问题。
In experimental design, a tacit principle is that to test whether a candidate cause c (i.e., a manipulation) prevents an effect e, e must occur at least some of the time without the introduction of c. This principle is the preventive analogue of the explicit principle of avoiding a ceiling effect in tests of whether c produces e. Psychological models of causal inference that adopt either the covariation approach or the power approach, among their other problems, fail to explain these principles. The present article reports an experiment that demonstrates the operation of these principles in untutored reasoning. The results support an explanation of these principles according to the power PC theory, a theory that integrates the previous approaches to overcome the problems that cripple each.