From covariation to causation: A causal power theory
From covariation to causation: A causal power theory
复制标题
DOI：
10.1037/0033-295x.104.2.367
复制
发表时间：
1997-04-01
影响因子：
5.4
通讯作者：
Cheng, PW
中科院分区：
文献类型：
--
作者：
Cheng, PW
因为因果关系既不可观察也不可推断，所以它们必须从可观察事件中归纳出来。因果归纳心理学的两种主要方法--共变方法和因果力量方法--都被基本问题所削弱。本文提出了一个集成这些方法，克服这些问题。这个建议是，推理者天生就把协变（一个根据可观察事件定义的函数）和因果力（一个不可观察的实体）之间的关系看作是科学家的定律或模型与他们解释模型的理论之间的关系。这个解决方案是形式化的功率PC理论，因果功率理论的概率对比模型（P。Cheng & L. R. Novick，1990）。文章回顾了各种新旧实证检验区分这一理论从以前的模型，没有一个是合理的理论。结果唯一地支持了功率PC理论。
Because causal relations are neither observable nor deducible, they must be induced from observable events. The 2 dominant approaches to the psychology of causal induction-the covariation approach and the causal power approach-are each crippled by fundamental problems. This article proposes an integration of these approaches that overcomes these problems. The proposal is that reasoners innately treat the relation between covariation (a function defined in terms of observable events) and causal power(an unobservable entity) as that between scientists' law or model and their theory explaining the model. This solution is formalized in the power PC theory, a causal power theory of the probabilistic contrast model(P. W. Cheng & L. R. Novick, 1990). The article reviews diverse old and new empirical tests discriminating this theory from previous models, none of which is justified by a theory. The results uniquely support the power PC theory.