Logo Logo
Hilfe
Kontakt
Switch language to English
Causal explanations - how to generate, identify, and evaluate them. a causal model approach augmented with causal powers
Causal explanations - how to generate, identify, and evaluate them. a causal model approach augmented with causal powers
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.
Causal Explanation, Explanation, Causation, Causal Power, Explanatory Power, Proportionality
Borner, Jan
2023
Englisch
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Borner, Jan (2023): Causal explanations - how to generate, identify, and evaluate them: a causal model approach augmented with causal powers. Dissertation, LMU München: Fakultät für Philosophie, Wissenschaftstheorie und Religionswissenschaft
[thumbnail of Borner_Jan.pdf] PDF
Borner_Jan.pdf

6MB

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.