SOPhiA 2022

Salzburgiense Concilium Omnibus Philosophis Analyticis

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Programm - Vortrag

Causal Power Quantified - A Generalization and Defense of Cheng's Causal Power Measure
(Philosophy of Science, Englisch)

As part of her power PC theory, Patricia Cheng (1997) has introduced a measure of probabilistic causal power, which is supposed to quantify the capacity of a cause to produce its effect. Cheng__s measure is not the only measure of probabilistic causal strength out there. But while there is variety of different proposals in the literature (Eells (1991), Suppes (1970), Lewis (1986)), Fitelson and Hitchcock (2011) have convincingly argued that Cheng's measure is the most suitable explication of intrinsic causal power, a concept that is highly valuable when it comes to predictions and decision making, since the intrinsic causal power of a cause is supposed to remain stable over different contexts. Additionally, Cheng and her colleagues have shown in several experiments that her measure is an accurate description of how humans actually reason about causal relationships (see, for example, Liljeholm and Cheng (2007)).__

Despite all that, several arguments have recently emerged that challenge the adequacy and viability of Cheng's measure. Most notably, Sprenger (2018) argues that any measure that is not ordinally equivalent to Eells_ measure of causal strength is deficient in the sense that it does not satisfy some basic and highly intuitive adequacy constraints. I want to defend Cheng's measure from Sprenger's arguments. But to do so, Cheng's power PC theory has to be generalized to make her measure of causal power applicable to more complex situations than those that it was originally designed for. I will argue that this can be done in a straightforward way.

Chair: Maren Bräutigam
Zeit: 17:30-18:00, 07. September 2022 (Mittwoch)
Ort: SR 1.004

Jan Borner
(MCMP, LMU Munich, Deutschland)

Testability and Meaning deco