The three faces of faithfulness.
In the causal inference framework of Spirtes, Glymour, and Scheines (SGS), inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the...
| Publicado en: | Synthese Vol. 193; no. 4; pp. 1011 - 1028 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Apr2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=114677302&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 114677302 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Apr2016 vid: 193 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 114677302 10.1007/s11229-015-0673-9 ppf: 1011 ppct: 17 formats: fmt: @attributes: type: P size: 527KB tig: atl: The three faces of faithfulness. aug: au: Zhang, Jiji Spirtes, Peter affil: Department of Philosophy, Lingnan University, Room HSH201, Ho Sin Hang Building Tuen Mun Hong Kong Department of Philosophy, Carnegie Mellon University, 135D Baker Hall, 5000 Forbes Avenue Pittsburgh 15213 USA su: Causal models Distribution (Probability theory) Mathematical variables Parameter estimation Bayesian analysis sug: subj: Causal models Distribution (Probability theory) Mathematical variables Parameter estimation Bayesian analysis keyword: Bayes nets Causal inference Faithfulness Graphical models ab: In the causal inference framework of Spirtes, Glymour, and Scheines (SGS), inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the Causal Faithfulness Assumption, which roughly states that if a conditional independence statement is true of a probability distribution generated by a causal structure, it is entailed by the causal structure and not just for particular parameter values. In this paper we show that the addition of the Causal Faithfulness Assumption plays three quite different roles in the SGS framework: (i) it reduces the degree of underdetermination of causal structure by probability distribution; (ii) computationally, it justifies reliable (constraint-based) causal inference algorithms that would otherwise have to be slower in order to be reliable; and (iii) statistically, it implies that those algorithms reliably obtain the correct answer at smaller sample sizes than would otherwise be the case. We also consider a number of variations on the Causal Faithfulness Assumption, and show how they affect each of these three roles. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2016. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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