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Constitution and Causal Roles

Casini, Lorenzo and Baumgartner, Michael (2019) Constitution and Causal Roles. [Preprint]

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Abstract

Alexander Gebharter (2017b) has proposed to use Bayesian network (BN)
causal discovery methods to identify the constitutive dependencies underwriting
mechanistic explanations. The account assumes that mechanistic constitution
behaves like deterministic direct causation, such that BN methods are
directly applicable to mixed variable sets featuring both causal and constitutive
dependencies. Gebharter claims that such mixed sets, under certain restrictions,
comply with the assumptions of the causal BN framework. 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 BN assumptions. In the second half, we present an alternative way to
bring BN tools to bear on the discovery of mechanistic constitution. More precisely,
we argue that all of a phenomenon’s parts, whose causal roles 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
Creators:
CreatorsEmailORCID
Casini, Lorenzolorenzo.casini@unige.ch0000-0001-9891-7324
Baumgartner, Michaelmichael.baumgartner@uib.no
Keywords: mechanistic explanation; constitution; causal role; Bayesian network; supervenience.
Subjects: General Issues > Causation
General Issues > Explanation
Depositing User: Dr. Lorenzo Casini
Date Deposited: 01 Apr 2019 15:38
Last Modified: 01 Apr 2019 15:38
Item ID: 15868
Subjects: General Issues > Causation
General Issues > Explanation
Date: 21 January 2019
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/15868

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