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Green and grue causal variables

Eberhardt, Frederick (2016) Green and grue causal variables. Synthese, 193 (4). pp. 1029-1046. ISSN 0039-7857. doi:10.1007/s11229-015-0832-z. https://resolver.caltech.edu/CaltechAUTHORS:20160602-123514680

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Abstract

The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores the set of causal variables that function as relata in these axioms. Spirtes (2007) showed how a causal system can be equivalently described by two different sets of variables that stand in a non-trivial translation-relation to each other, suggesting that there is no “correct” set of causal variables. I extend Spirtes’ result to the general framework of linear structural equation models and then explore to what extent the possibility to intervene or a preference for simpler causal systems may help in selecting among sets of causal variables.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1007/s11229-015-0832-zDOIArticle
http://link.springer.com/article/10.1007%2Fs11229-015-0832-zPublisherArticle
Additional Information:© 2016 Springer. S.I.: The Philosophy Of Clark Glymour. First online: 30 August 2015.
Subject Keywords:Causality – Intervention – Bayes nets – Variable definition
Issue or Number:4
DOI:10.1007/s11229-015-0832-z
Record Number:CaltechAUTHORS:20160602-123514680
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160602-123514680
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:67575
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:02 Jun 2016 19:46
Last Modified:11 Nov 2021 03:51

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