Causal Strength

J. Sprenger, S. Hartmann
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Abstract

The question “When is C a cause of E?” is well-studied in philosophy—much more than the equally important issue of quantifying the causal strength between C and E. In this chapter, we transfer methods from Bayesian Confirmation Theory to the problem of explicating causal strength. We develop axiomatic foundations for a probabilistic theory of causal strength as difference-making and proceed in three steps: First, we motivate causal Bayesian networks as an adequate framework for defining and comparing measures of causal strength. Second, we demonstrate how specific causal strength measures can be derived from a set of plausible adequacy conditions (method of representation theorems). Third, we use these results to argue for a specific measure of causal strength: the difference that interventions on the cause make for the probability of the effect. An application to outcome measures in medicine and discussion of possible objections concludes the chapter.
因果强度
“什么时候C是E的原因?”这个问题在哲学上得到了很好的研究——远远超过同样重要的量化C和e之间因果强度的问题。在本章中,我们将贝叶斯确认理论的方法转移到解释因果强度的问题上。我们为因果强度的概率理论发展了公理基础,并分三步进行:首先,我们激励因果贝叶斯网络作为定义和比较因果强度度量的适当框架。其次,我们展示了如何从一组似是而非的充分性条件(表示定理方法)中推导出具体的因果强度度量。第三,我们使用这些结果来论证因果强度的具体度量:对原因的干预对效果概率的影响。在医学结果测量和可能的反对意见的讨论中的应用结束了本章。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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