The PC Algorithm and the Inference to Constitution

Bibliographic Data

ID8396252
AuthorsLorenzo Casini (0000-0003-3075-8501, University of Geneva, corresponding author), Michael Baumgartner (0000-0003-1536-2816, University of Bergen)
Year2023
Volume74
Issue2
Pages405-429
Publication date2023-06-01
Open AccessNo
TypeARTICLE
VenueThe British Journal for the Philosophy of Science (JOURNAL)
Journal identifiersISSN: 0007-0882 • E-ISSN: 1464-3537
PublisherUniversity of Chicago Press (PUBLISHER • US)
DOI10.1086/714820
OpenAlexW3046410230
LanguageEN
Citations received2
References cited21

Gebharter has proposed using one of the best known Bayesian network causal discovery algorithms, PC, to identify the constitutive dependencies underwriting mechanistic explanations. His proposal assumes that mechanistic constitution behaves like deterministic direct causation, such that PC is directly applicable to mixed variable sets featuring both causal and constitutive dependencies. Gebharter claims that such mixed sets, under certain restrictions, comply with PC’s background assumptions. The aim of this article is to show that Gebharter’s proposal incurs severe problems, ultimately rooted in the widespread non-compliance of mechanistic systems with PC’s assumptions. This casts severe doubts on the attempt to implicitly define constitution as a form of deterministic direct causation complying with PC’s assumptions

Algorithm · Constitution · Epistemology · Inference · Political science · AI-based Problem Solving and Planning · Artificial Intelligence · Computability, Logic, AI Algorithms · Computer Science · Evolutionary Algorithms and Applications · Law · Philosophy

Tools

Open DOIOpen Access