Causal explanations - how to generate, identify, and evaluate them

Dissertation, Ludwig Maximilians Universität, München (2023)
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Abstract

The main goal of this dissertation is to provide a solid foundation for a formalization of Inference to the Best Explanation (IBE). This foundation consists of three major components. First, an intuitively adequate and formally precise model of causal explanation. Secondly, an intuitively adequate and formally precise measure of (causal) explanatory power. And third, an intuitively adequate and formally precise criterion of proportionality that is able to identify the most appropriate level of specificity for a causal explanation. While the first component makes it possible to generate and identify causal explanations reliably, the second and third components make it possible to evaluate the strength or quality of causal explanations, which is crucial for identifying the best of a set of competing causal explanations.

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2023-04-29

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