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Diagnosis under compound effects and multiple causes by means of the conditional causal possibility approach

机译:通过条件因果可能性方法进行复合效应和多种原因的诊断

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The paper addresses uncertain reasoning on a causal model given by two layered networks, where nodes in one layer express possible causes and those in the other are possible effects. Uncertainty of causalities is expressed in a novel manner, i.e. by Conditional Causal Possibilities. The expression has two advantages over the conventional way with conditional possibilities: it expresses the exact degrees of possibility of causalities, and the number of necessary conditional causal possibilities is far smaller than that of conditional possibilities. However, it has a weakness that it cannot handle causalities with compound effects such as synergistic and canceling effects by multiple causes. The paper discusses the weakness and proposes a solution. First, it discusses how to deal with the compound effects and proposes a new causal model with conditional causal possibilities by multiple causes. Then, it defines a causality consistency problem that calculates possibility of a hypothesis given some observed events, and shows a way to solve the problem.
机译:本文讨论了由两层网络给出的因果模型上的不确定推理,其中一层中的节点表示可能的原因而另一层中的节点表示可能的结果。因果的不确定性以新颖的方式表达,即通过条件因果可能性来表达。与具有条件可能性的传统方式相比,该表达式具有两个优点:它表达了因果关系可能性的确切程度,并且必要的条件因果可能性的数量远远小于条件因果可能性的数量。但是,它有一个缺点,即它无法处理因多种原因而产生的因果效应,例如协同效应和抵消效应。本文讨论了缺点并提出了解决方案。首先,它讨论了如何处理复合效应,并提出了一种具有多种原因的条件因果关系的新因果模型。然后,它定义了因果一致性问题,该问题可以计算给定一些观察到的事件的假设可能性,并提供解决该问题的方法。

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