The role of causation in measuring explanatory power

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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.