Structure and strength in causal induction
Structure and strength in causal induction
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DOI：
10.1016/j.cogpsych.2005.05.004
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发表时间：
2005-12-01
影响因子：
2.6
通讯作者：
Tenenbaum, JB
中科院分区：
文献类型：
--
作者：
Griffiths, TL;Tenenbaum, JB
我们提出了一个对基本因果归纳进行理性分析的框架——基于因果图模型学习单一原因和结果之间关系的存在。这个框架精确地区分了因果结构和因果强度之间的区别：询问因果关系是否存在和询问因果关系可能有多强之间的区别。我们证明了基本因果归纳的两个主要理性模型，Delta P 和因果功率，都可以估计因果强度，并且我们引入了一种新的理性模型，即因果支持，用于评估因果结构。因果支持预测了因果归纳的几个关键现象，这些现象无法用其他理性模型来解释，我们通过一系列实验对此进行了探索。这些现象包括在没有原因的情况下 AP 与该效应的基本概率之间的复杂相互作用、样本量效应、不完整列联表的推论以及从比率进行因果学习。与 AP 或因果功效相比，因果支持还可以更好地解释许多现有数据集。 (c) 2005 Elsevier Inc. 保留所有权利。
We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, Delta P and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between AP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either AP or causal power. (c) 2005 Elsevier Inc. All rights reserved.