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

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

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.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Keywords: Causal Explanation, Explanation, Causation, Causal Power, Explanatory Power, Proportionality
• Themengebiete: 100 Philosophie und Psychologie ; 100 Philosophie und Psychologie > 190 Moderne westliche Philosophie
• Fakultäten: Fakultät für Philosophie, Wissenschaftstheorie und Religionswissenschaft
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 13. Februar 2023
• 1. Bericht­erstatter:in: Leitgeb, Hannes