The role of causation in measuring explanatory power

科学家试图解释现象。例如，流行病学家解释了人们呼吸受感染的空气传播病毒的原因。然而，通常对同一现象有几种解释。接触污染表面的人也可以解释传播。那么，什么能最好地解释一种现象呢？哲学家们已经提出了解释什么是好的解释。然而，这些缺乏形式上的精确性。这种精确性很重要：它使我们能够准确地评估任何解释对某种现象的解释程度。这样的评估可以指导科学家，并澄清关于什么是好的解释的争论。有鉴于此，哲学家们开始为解释力提供形式上精确的衡量标准。目前，这些测量解释力的文献缺乏对因果关系作用的讨论。然而，从表面上看，因果关系在这样一个测量项目中起着决定性的作用：通常，但并不总是，科学家通过引用原因来解释现象。但是，解释力的度量应该在什么时候以及如何（如果有的话）解释因果关系在解释中的作用呢？在这个博士项目中，我提供了缺失的答案。我调查了三个开放的问题因果关系在衡量解释力的作用，并得出周围的最佳解释推理辩论的影响。

Scientists aim to explain phenomena. Epidemiologists, for instance, explain the spreading of a virus by people breathing infected air. Usually, however, there are several explanations for the same phenomenon. People touching contaminated surfaces could also explain the spreading. But what then explains a phenomenon best? Philosophers have proposed accounts for what a good explanation is. Yet, these lack formal precision. And such precision matters: It allows us to accurately assess how well any explanation explains some phenomenon. Such assessments can guide scientists as well as clarify debates on what it means to explain well. In light of this, philosophers started to provide formally precise measures for explanatory power. At present, this literature on measuring explanatory power lacks a discussion of the role of causation. Prima facie, though, causation plays a decisive role in such a measurement project: Often, yet not always,scientists explain phenomena by citing their causes. But when and how (if at all) should measures for explanatory power account for this role of causation in explaining? In this PhD project, I provide the missing answer. I investigate three open questions on the role of causation in measuring explanatory power and draw implications for debates surrounding inference to the best explanation.