Casini, Lorenzo and Baumgartner, Michael (2019) Constitution and Causal Roles. [Preprint]
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Abstract
Gebharter (2017b) has proposed to use one of the best known Bayesian network(BN) causal discovery algorithms, PC, to identify the constitutive dependencies munderwriting 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 paper is twofold. In the first half, we argue that Gebharter’s proposal incurs severe problems, ultimately rooted in widespread non-compliance of mechanistic systems with PC’s assumptions. In the second half, we present an alternative way to bring PC to bear on the discovery of mechanistic constitution. More precisely, we argue that all of the parts of a phenomenon that account for why the phenomenon has its characteristic causal role are constituents—where the notion of causal role is probabilistically understood.
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| Item Type: | Preprint | |||||||||
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| Keywords: | mechanistic explanation; constitution; causal role; Bayesian network; supervenience; PC. | |||||||||
| Subjects: | General Issues > Causation General Issues > Explanation |
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| Depositing User: | Dr. Lorenzo Casini | |||||||||
| Date Deposited: | 26 Dec 2019 07:40 | |||||||||
| Last Modified: | 26 Dec 2019 07:40 | |||||||||
| Item ID: | 16747 | |||||||||
| Subjects: | General Issues > Causation General Issues > Explanation |
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| Date: | 24 September 2019 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/16747 |
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